
R version 3.3.0 (2016-05-03) -- "Supposedly Educational"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> # last change: 2017-05-18
> 
> 
> # ==============================================================================
> # Prepare workspace
> # ==============================================================================
> 
> library("network")
network: Classes for Relational Data
Version 1.13.0 created on 2015-08-31.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
                    Mark S. Handcock, University of California -- Los Angeles
                    David R. Hunter, Penn State University
                    Martina Morris, University of Washington
                    Skye Bender-deMoll, University of Washington
 For citation information, type citation("network").
 Type help("network-package") to get started.

> library("sna")
sna: Tools for Social Network Analysis
Version 2.3-2 created on 2014-01-13.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
 For citation information, type citation("sna").
 Type help(package="sna") to get started.


Attaching package: ‘sna’

The following object is masked from ‘package:network’:

    %c%

> library("ergm")
Loading required package: statnet.common

ergm: version 3.6.0, created on 2016-03-24
Copyright (c) 2016, Mark S. Handcock, University of California -- Los Angeles
                    David R. Hunter, Penn State University
                    Carter T. Butts, University of California -- Irvine
                    Steven M. Goodreau, University of Washington
                    Pavel N. Krivitsky, University of Wollongong
                    Martina Morris, University of Washington
                    with contributions from
                    Li Wang
                    Kirk Li, University of Washington
                    Skye Bender-deMoll, University of Washington
Based on "statnet" project software (statnet.org).
For license and citation information see statnet.org/attribution
or type citation("ergm").

NOTE: If you use custom ERGM terms based on ‘ergm.userterms’ version
prior to 3.1, you will need to perform a one-time update of the package
boilerplate files (the files that you did not write or modify) from
‘ergm.userterms’ 3.1 or later. See help('eut-upgrade') for
instructions.

> library("xergm")
Loading required package: xergm.common

Attaching package: ‘xergm.common’

The following object is masked from ‘package:ergm’:

    gof

Loading required package: btergm
Loading required package: ggplot2
Package:  btergm
Version:  1.9.0
Date:     2017-03-30
Authors:  Philip Leifeld (University of Glasgow)
          Skyler J. Cranmer (The Ohio State University)
          Bruce A. Desmarais (Pennsylvania State University)

Loading required package: tnam
Package:  tnam
Version:  1.6.5
Date:     2017-03-31
Authors:  Philip Leifeld (University of Glasgow)
          Skyler J. Cranmer (The Ohio State University)

Loading required package: rem
Loading required package: GERGM
GERGM: Generalized Exponential Random Graph Models
Version 0.11.2 created on 2017-03-14.
copyright (c) 2017, Matthew J. Denny, Penn State University
                    James D. Wilson, University of San Francisco
                    Skyler Cranmer, Ohio State University
                    Bruce A. Desmarais, Penn State University
                    Shankar Bhamidi, University of North Carolina
Type help('gergm') to get started.
Development website: https://github.com/matthewjdenny/GERGM
Package:  xergm
Version:  1.8.2
Date:     2017-04-01
Authors:  Philip Leifeld (University of Glasgow)
          Skyler J. Cranmer (The Ohio State University)
          Bruce A. Desmarais (Pennsylvania State University)

Please cite the xergm package in your publications -- see citation("xergm").

Warning message:
replacing previous import ‘network::%c%’ by ‘sna::%c%’ when loading ‘statnet’ 
> library("texreg")
Version:  1.36.23
Date:     2017-03-03
Author:   Philip Leifeld (University of Glasgow)

Please cite the JSS article in your publications -- see citation("texreg").
> library("inline")
> library("Rcpp")

Attaching package: ‘Rcpp’

The following object is masked from ‘package:inline’:

    registerPlugin

> library("ggplot2")
> library("reshape2")
> 
> sessionInfo()
R version 3.3.0 (2016-05-03)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.2 LTS

locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
 [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] reshape2_1.4.1       Rcpp_0.12.3          inline_0.3.14       
 [4] texreg_1.36.23       xergm_1.8.2          GERGM_0.11.2        
 [7] rem_1.1.2            tnam_1.6.5           btergm_1.9.0        
[10] ggplot2_2.0.0        xergm.common_1.7.7   ergm_3.6.0          
[13] statnet.common_3.3.0 sna_2.3-2            network_1.13.0      

loaded via a namespace (and not attached):
 [1] deSolve_1.12         gtools_3.5.0         lpSolve_5.6.13      
 [4] ergm.count_3.2.0     splines_3.3.0        lattice_0.20-33     
 [7] mstate_0.2.8         colorspace_1.2-6     flexsurv_0.7        
[10] stats4_3.3.0         tergm_3.4.0          mgcv_1.8-12         
[13] survival_2.39-4      nloptr_1.0.4         RColorBrewer_1.1-2  
[16] muhaz_1.2.6          speedglm_0.3-1       trust_0.1-7         
[19] plyr_1.8.3           stringr_1.0.0        robustbase_0.92-5   
[22] munsell_0.4.2        gtable_0.1.2         caTools_1.17.1      
[25] mvtnorm_1.0-5        coda_0.18-1          permute_0.8-4       
[28] parallel_3.3.0       DEoptimR_1.0-4       KernSmooth_2.23-15  
[31] ROCR_1.0-7           networkDynamic_0.9.0 statnet_2016.4      
[34] scales_0.3.0         gdata_2.17.0         vegan_2.3-1         
[37] RcppParallel_4.3.20  lme4_1.1-10          gplots_2.17.0       
[40] stringi_1.0-1        grid_3.3.0           quadprog_1.5-5      
[43] tools_3.3.0          bitops_1.0-6         magrittr_1.5        
[46] RSiena_1.1-232       cluster_2.0.4        MASS_7.3-45         
[49] Matrix_1.2-6         minqa_1.2.4          boot_1.3-17         
[52] igraph_1.0.1         nlme_3.1-128        
> # R version 3.3.0 (2016-05-03)
> # Platform: x86_64-pc-linux-gnu (64-bit)
> # Running under: Ubuntu 16.04.2 LTS
> # 
> # locale:
> #  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
> #  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
> #  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
> #  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
> #  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
> # [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
> # 
> # attached base packages:
> # [1] stats     graphics  grDevices utils     datasets  methods   base     
> # 
> # other attached packages:
> #  [1] reshape2_1.4.1       Rcpp_0.12.3          inline_0.3.14       
> #  [4] texreg_1.36.23       xergm_1.8.2          GERGM_0.11.2        
> #  [7] rem_1.1.2            tnam_1.6.5           btergm_1.9.0        
> # [10] ggplot2_2.0.0        xergm.common_1.7.7   ergm_3.6.0          
> # [13] statnet.common_3.3.0 sna_2.3-2            network_1.13.0      
> # 
> # loaded via a namespace (and not attached):
> #  [1] deSolve_1.12         gtools_3.5.0         lpSolve_5.6.13      
> #  [4] ergm.count_3.2.0     splines_3.3.0        lattice_0.20-33     
> #  [7] mstate_0.2.8         colorspace_1.2-6     flexsurv_0.7        
> # [10] stats4_3.3.0         tergm_3.4.0          mgcv_1.8-12         
> # [13] survival_2.39-4      nloptr_1.0.4         RColorBrewer_1.1-2  
> # [16] muhaz_1.2.6          speedglm_0.3-1       trust_0.1-7         
> # [19] plyr_1.8.3           stringr_1.0.0        robustbase_0.92-5   
> # [22] munsell_0.4.2        gtable_0.1.2         caTools_1.17.1      
> # [25] mvtnorm_1.0-5        coda_0.18-1          permute_0.8-4       
> # [28] parallel_3.3.0       DEoptimR_1.0-4       KernSmooth_2.23-15  
> # [31] ROCR_1.0-7           networkDynamic_0.9.0 statnet_2016.4      
> # [34] scales_0.3.0         gdata_2.17.0         vegan_2.3-1         
> # [37] RcppParallel_4.3.20  lme4_1.1-10          gplots_2.17.0       
> # [40] stringi_1.0-1        grid_3.3.0           quadprog_1.5-5      
> # [43] tools_3.3.0          bitops_1.0-6         magrittr_1.5        
> # [46] RSiena_1.1-232       cluster_2.0.4        MASS_7.3-45         
> # [49] Matrix_1.2-6         minqa_1.2.4          boot_1.3-17         
> # [52] igraph_1.0.1         nlme_3.1-128
> 
> burnin <- 10000      # MCMC burnin
> sampsize <- 10000    # MCMC sample size
> maxit <- 200         # number of MCMC MLE iterations
> nsim <- 1000         # number of simulated networks for the GOF assessment
> cores <- 3           # number of computing cores for parallel processing
> seed <- 12345        # random seed for exact reproducibility
> set.seed(seed)
> 
> 
> # ==============================================================================
> # Read CSV files and transform/manage data
> # ==============================================================================
> 
> # leadership network
> leader <- as.matrix(read.csv("Coalition_Leadership.csv", header = TRUE, 
+     row.names = 1, stringsAsFactors = FALSE))
> 
> # coalition non-membership matrix
> nonmem <- as.matrix(read.csv("Coalition_Nonmembership.csv", header = TRUE, 
+     row.names = 1, stringsAsFactors = FALSE))
> mem <- (nonmem * -1) + 1  # membership matrix
> 
> # several nodal attributes
> attrib <- read.csv("Coalition_Node_Attributes.csv", header = TRUE)
> 
> # communication network: any kind of communication
> comm.any <- as.matrix(read.table("Communication_Any.csv", 
+     stringsAsFactors = FALSE, sep = ",", header = TRUE, row.names = 1))
> 
> # communication network: occasional communication
> comm.occ <- as.matrix(read.table("Communication_Occasional.csv", 
+     stringsAsFactors = FALSE, sep = ",", header = TRUE, row.names = 1))
> 
> # communication network: regular communication
> comm.reg <- as.matrix(read.table("Communication_Regular.csv", 
+     stringsAsFactors = FALSE, sep = ",", header = TRUE, row.names = 1))
> 
> # attributes contain both groups and coalitions; they need to be separated
> attrib.grp <- attrib[1:nrow(mem), ]
> attrib.coal <- attrib[(nrow(mem) + 1): nrow(attrib), ]
> 
> # who founded which coalition?
> founded <- as.matrix(read.csv("Founded.csv", header = TRUE, row.names = 1, 
+     stringsAsFactors = FALSE))
> 
> 
> # ==============================================================================
> # Create new model terms for multiplexity and diversity
> # ==============================================================================
> 
> 
> # impute NA values in communication
> for (i in 1:nrow(comm.any)) {
+   for (j in 1:ncol(comm.any)) {
+     if (is.na(comm.any[i, j]) && !is.na(comm.any[j, i])) {
+       comm.any[i, j] <- comm.any[j, i]  # impute from reciprocal dyad
+     } else if (is.na(comm.any[j, i])) {
+       comm.any[i, j] <- 0  # zero-impute if reciprocal dyad also NA
+     }
+     if (is.na(comm.reg[i, j]) && !is.na(comm.reg[j, i])) {
+       comm.reg[i, j] <- comm.reg[j, i]
+     } else if (is.na(comm.reg[j, i])) {
+       comm.reg[i, j] <- 0
+     }
+     if (is.na(comm.occ[i, j]) && !is.na(comm.occ[j, i])) {
+       comm.occ[i, j] <- comm.occ[j, i]
+     } else if (is.na(comm.occ[j, i])) {
+       comm.occ[i, j] <- 0
+     }
+   }
+ }
> 
> 
> # H1: network embeddedness
> 
> # compute co-occurrence of coalition membership among coalition members
> cpp.comember.strong <- cxxfunction(signature(mat = "matrix"), plugin = "Rcpp", 
+     body = '
+   IntegerMatrix mem = as<IntegerMatrix>(mat);
+   int rows = mem.nrow();
+   int cols = mem.ncol();
+   Rcpp::NumericMatrix comemb = NumericMatrix(rows, cols);
+   int realized;
+   int possible;
+   for (int i = 0; i < cols; i++) {
+     for (int j = 0; j < rows; j++) {
+       realized = 0;
+       possible = 0;
+       for (int k = 0; k < rows; k++) {
+         for (int l = 0; l < cols; l++) {
+           if (j != k && i != l && mem(j, i) == 1 && mem(k, i) == 1) {
+             possible++;
+             if (mem(k, l) == 1 && mem(j, l) == 1) {
+               realized++;
+             }
+           }
+         }
+       }
+       //std::cout << i << " " << j << " " << realized << " " << possible << "\\n";
+       if (possible == 0.0) {
+         comemb(j, i) = 0.0;
+       } else {
+         comemb(j, i) = double(realized) / double(possible);
+       }
+     }
+   }
+   return(wrap(comemb));
+ ')
> 
> Network_Embeddedness_Strong <- cpp.comember.strong(mem)
> 
> # compute share of other members with whom i has at least one co-membership
> cpp.comember.weak <- cxxfunction(signature(mat = "matrix"), plugin = "Rcpp", 
+     body = '
+   IntegerMatrix mem = as<IntegerMatrix>(mat);
+   int rows = mem.nrow();
+   int cols = mem.ncol();
+   Rcpp::NumericMatrix comemb = NumericMatrix(rows, cols);
+   int realized;
+   int nummembers;
+   for (int i = 0; i < cols; i++) {
+     nummembers = 0;
+     for (int k = 0; k < rows; k++) {
+       if (mem(k, i) == 1) {
+         nummembers++;
+       }
+     }
+     for (int j = 0; j < rows; j++) {
+       realized = 0;
+       bool kiscomem;
+       for (int k = 0; k < rows; k++) {
+         kiscomem = false;
+         for (int l = 0; l < cols; l++) {
+           if (j != k && i != l && mem(j, i) == 1 && mem(k, i) == 1) {
+             if (mem(k, l) == 1 && mem(j, l) == 1) {
+               kiscomem = true;
+               realized++;
+               break;
+             }
+           }
+         }
+       }
+       //std::cout << i << " " << j << " " << realized << " " << nummembers << "\\n";
+       if (nummembers < 2.0) {
+         comemb(j, i) = 0.0;
+       } else {
+         comemb(j, i) = double(realized) / double(nummembers - 1);
+       }
+     }
+   }
+   return(wrap(comemb));
+ ')
> 
> Network_Embeddedness_Weak <- cpp.comember.weak(mem)
> 
> 
> # communication density of others in current coalition
> cpp.commdensity <- cxxfunction(signature(mat = "matrix", comm = "matrix"), 
+     plugin = "Rcpp", body = '
+   IntegerMatrix mem = as<IntegerMatrix>(mat);
+   IntegerMatrix com = as<IntegerMatrix>(comm);
+   int rows = mem.nrow();
+   int cols = mem.ncol();
+   Rcpp::NumericMatrix cd = NumericMatrix(rows, cols);
+   int realized;
+   int possible;
+   for (int i = 0; i < cols; i++) {
+     for (int j = 0; j < rows; j++) {
+       realized = 0;
+       possible = 0;
+       for (int k = 0; k < rows; k++) {
+         if (j != k && mem(j, i) == 1 && mem(k, i) == 1) {
+           possible++;
+           if (com(j, k) == 1) {
+             realized++;
+           }
+         }
+       }
+       // std::cout << realized << " " << possible << "\\n";
+       if (possible == 0.0) {
+         cd(j, i) = 0.0;
+       } else {
+         cd(j, i) = double(realized) / double(possible);
+       }
+     }
+   }
+   return(wrap(cd));
+ ')
> 
> commdensity <- cpp.commdensity(mem, comm.any)
> 
> 
> # H2: diversity
> 
> cpp.diversity <- cxxfunction(signature(mat = "matrix", attribute = "integer"), 
+     plugin = "Rcpp", body = '
+   IntegerMatrix mem = as<IntegerMatrix>(mat);
+   IntegerVector at = as<IntegerVector>(attribute);
+   int rows = mem.nrow();
+   int cols = mem.ncol();
+   Rcpp::NumericMatrix diversity = NumericMatrix(rows, cols);
+   for (int i = 0; i < cols; i++) {
+     for (int j = 0; j < rows; j++) {
+       double sum = 0.0;
+       int counter = 0;
+       for (int k = 0; k < rows; k++) {
+         if (mem(j, i) == 1 && mem(k, i) == 1) {
+           counter++;
+           sum = sum + at[k];
+         }
+       }
+       double mean = sum / counter;
+       double sqsum = 0.0;
+       for (int k = 0; k < rows; k++) {
+         if (mem(j, i) == 1 && mem(k, i) == 1) {
+           sqsum = sqsum + ((mean - at[k]) * (mean - at[k]));
+         }
+       }
+       if (counter == 0) {
+         diversity(j, i) = 0;
+       } else {
+         diversity(j, i) = sqrt(sqsum / counter);
+       }
+     }
+   }
+   return(wrap(diversity));
+ ')
> 
> # diversity measures
> Partisan_Diversity <- cpp.diversity(mat = mem, 
+     attribute = attrib.grp$Conservative_Lean_of_Organization)
> diversity.lobspend <- cpp.diversity(mat = mem, 
+     attribute = attrib.grp$Lobbying_Spending_by_Organization)
> diversity.infrep <- cpp.diversity(mat = mem, 
+     attribute = attrib.grp$Organizations_Influence_Reputation)
> diversity.outshealth <- cpp.diversity(mat = mem, 
+     attribute = attrib.grp$Organization_Identified_Primarily_Outside_Health)
> diversity.citadv <- cpp.diversity(mat = mem, 
+     attribute = attrib.grp$Organization_is_Citizens_Advocacy_Organization)
> diversity.age <- cpp.diversity(mat = mem, 
+     attribute = attrib.grp$Years_Since_Founding_of_Organization_Coalition)
> 
> # interaction terms comembership * diversity
> Diversity_X_Embeddedness_Strong <- 
+     Network_Embeddedness_Strong * Partisan_Diversity
> Diversity_X_Embeddedness_Weak <- 
+     Network_Embeddedness_Weak * Partisan_Diversity
> commdensity.diversity <- commdensity * Partisan_Diversity
> 
> # dependent variable and structural zeros and ones
> leader <- network(leader, directed = FALSE, bipartite = TRUE)  # DV
> # model 1: non-members are structural zeros:
> nonmem <- network(nonmem, directed = FALSE, bipartite = TRUE)
> nonmem2 <- as.matrix(nonmem)
> nonmem2[founded == 1] <- 1  # model 2, non-members and founders = struct. zeros
> sum(mem * founded)  # 136 additional structural zeros
[1] 136
> sum((mem * founded) * as.matrix(leader))  # 102 leadership ties are removed
[1] 102
> # model 3: in addition to nonmem as structural zeros, model founders who are 
> # also leaders as structural ones, i.e., constrain founders to be leaders:
> founderleader <- as.matrix(leader) * founded
> 
> 
> # ==============================================================================
> # Create model terms for control variables
> # ==============================================================================
> 
> # issue controversy
> Controversial <- matrix(rep(attrib.coal$Issue_is_Highly_Controversial, 
+     nrow(as.matrix(leader))), nrow = nrow(as.matrix(leader)), byrow = TRUE)
> Diversity_X_Controversial <- Partisan_Diversity * Controversial
> 
> # coalition visibility
> Visibility <- matrix(rep(attrib.coal$Coalition_in_Public, 
+     nrow(as.matrix(leader))), nrow = nrow(as.matrix(leader)), byrow = TRUE)
> Diversity_X_Visibility <- Partisan_Diversity * Visibility
> 
> # three-way interaction: diversity x embeddedness within or across parties
> cpp.comember.strong.party <- cxxfunction(signature(mat = "matrix", 
+     cl = "IntegerVector", cross = "bool"), plugin = "Rcpp", body = '
+   IntegerMatrix mem = as<IntegerMatrix>(mat);
+   IntegerVector conslean = as<IntegerVector>(cl);
+   bool crossparty = as<bool>(cross);
+   int rows = mem.nrow();
+   int cols = mem.ncol();
+   Rcpp::NumericMatrix comemb = NumericMatrix(rows, cols);
+   int realized;
+   int possible;
+   for (int i = 0; i < cols; i++) {
+     for (int j = 0; j < rows; j++) {
+       realized = 0;
+       possible = 0;
+       for (int k = 0; k < rows; k++) {
+         if ((crossparty == false && (conslean(j) < 0 && conslean(k) < 0) || 
+                                     (conslean(j) >= 0 && conslean(k) >= 0)) ||
+             (crossparty == true && (conslean(j) < 0 && conslean(k) >= 0) || 
+                                     (conslean(j) >= 0 && conslean(k) < 0))) {
+           for (int l = 0; l < cols; l++) {
+             if (j != k && i != l && mem(j, i) == 1 && mem(k, i) == 1) {
+               possible++;
+               if (mem(k, l) == 1 && mem(j, l) == 1) {
+                 realized++;
+               }
+             }
+           }
+         }
+       }
+       // std::cout << realized << " " << possible << "\\n";
+       if (possible == 0.0) {
+         comemb(j, i) = 0.0;
+       } else {
+         comemb(j, i) = double(realized) / double(possible);
+       }
+     }
+   }
+   return(wrap(comemb));
+ ')
> 
> cpp.comember.weak.party <- cxxfunction(signature(mat = "matrix", 
+     cl = "IntegerVector", cross = "bool"), plugin = "Rcpp", body = '
+   IntegerMatrix mem = as<IntegerMatrix>(mat);
+   IntegerVector conslean = as<IntegerVector>(cl);
+   bool crossparty = as<bool>(cross);
+   int rows = mem.nrow();
+   int cols = mem.ncol();
+   Rcpp::NumericMatrix comemb = NumericMatrix(rows, cols);
+   int realized;
+   int nummembers;
+   for (int i = 0; i < cols; i++) {
+     nummembers = 0;
+     for (int k = 0; k < rows; k++) {
+       if (mem(k, i) == 1) {
+         nummembers++;
+       }
+     }
+     for (int j = 0; j < rows; j++) {
+       realized = 0;
+       bool kiscomem;
+       for (int k = 0; k < rows; k++) {
+         kiscomem = false;
+         if ((crossparty == false && (conslean(j) < 0 && conslean(k) < 0) || 
+                                     (conslean(j) >= 0 && conslean(k) >= 0)) ||
+             (crossparty == true && (conslean(j) < 0 && conslean(k) >= 0) || 
+                                     (conslean(j) >= 0 && conslean(k) < 0))) {
+           for (int l = 0; l < cols; l++) {
+             if (j != k && i != l && mem(j, i) == 1 && mem(k, i) == 1) {
+               if (mem(k, l) == 1 && mem(j, l) == 1) {
+                 kiscomem = true;
+                 realized++;
+                 break;
+               }
+             }
+           }
+         }
+       }
+       //std::cout << i << " " << j << " " << realized << " " << nummembers << "\\n";
+       if (nummembers < 2.0) {
+         comemb(j, i) = 0.0;
+       } else {
+         comemb(j, i) = double(realized) / double(nummembers - 1);
+       }
+     }
+   }
+   return(wrap(comemb));
+ ')
> 
> Network_Embeddedness_Weak_SameParty <- cpp.comember.weak.party(mem, 
+     attrib.grp$Conservative_Lean_of_Organization_Coalition, FALSE)
> Network_Embeddedness_Weak_CrossParty <- cpp.comember.weak.party(mem, 
+     attrib.grp$Conservative_Lean_of_Organization_Coalition, TRUE)
> Network_Embeddedness_Strong_SameParty <- cpp.comember.strong.party(mem, 
+     attrib.grp$Conservative_Lean_of_Organization_Coalition, FALSE)
> Network_Embeddedness_Strong_CrossParty <- cpp.comember.strong.party(mem, 
+     attrib.grp$Conservative_Lean_of_Organization_Coalition, TRUE)
> Diversity_X_Embeddedness_Weak_SameParty <- 
+     Partisan_Diversity * Network_Embeddedness_Weak_SameParty
> Diversity_X_Embeddedness_CrossParty <- 
+     Partisan_Diversity * Network_Embeddedness_Weak_CrossParty
> 
> # Number_of_Coalition_Memberships: outdegree centrality of groups in the 
> # membership network
> rs <- rowSums(mem)
> Number_of_Coalition_Memberships <- matrix(NA, nrow = nrow(mem), 
+     ncol = ncol(mem))
> for (i in 1:nrow(Number_of_Coalition_Memberships)) {
+   Number_of_Coalition_Memberships[i, ] <- rs[i]
+ }
> 
> # Coalition_Size: indegree centrality of coalitions in the membership network
> cs <- colSums(mem)
> Coalition_Size <- matrix(NA, nrow = nrow(mem), ncol = ncol(mem))
> for (i in 1:ncol(Coalition_Size)) {
+   Coalition_Size[, i] <- cs[i]
+ }
> 
> # commpart.indeg: indegree centrality in the communication network; count number
> # of comm. partners in same coal. and divide by num. of coal. members excl. ego
> # (notes: NAs need to be replaced first; the matrix is transposed, i.e., 
> # communication flows from columns to rows, so this needs to be transposed)
> for (i in 1:nrow(comm.any)) {
+   for (j in 1:ncol(comm.any)) {
+     if (is.na(comm.any[i, j]) && !is.na(comm.any[j, i])) {
+       comm.any[i, j] <- comm.any[j, i]  # impute from reciprocal dyad
+     } else if (is.na(comm.any[j, i])) {
+       comm.any[i, j] <- 0  # zero-impute if reciprocal dyad also NA
+     }
+     if (is.na(comm.reg[i, j]) && !is.na(comm.reg[j, i])) {
+       comm.reg[i, j] <- comm.reg[j, i]
+     } else if (is.na(comm.reg[j, i])) {
+       comm.reg[i, j] <- 0
+     }
+     if (is.na(comm.occ[i, j]) && !is.na(comm.occ[j, i])) {
+       comm.occ[i, j] <- comm.occ[j, i]
+     } else if (is.na(comm.occ[j, i])) {
+       comm.occ[i, j] <- 0
+     }
+   }
+ }
> commpart.outdeg.any <- matrix(0, nrow = nrow(mem), ncol = ncol(mem))  # any com.
> commpart.indeg.any <- commpart.outdeg.any  # indegree, any type of communication
> commpart.outdeg.reg <- commpart.outdeg.any # outdegree, regular communication
> commpart.indeg.reg <- commpart.outdeg.any  # indegree, regular communication
> for (i in 1:nrow(mem)) {
+   for (j in 1:ncol(mem)) {
+     if (mem[i, j] == 1) {
+       members <- which(mem[, j] == 1)  # all members of this coalition
+       
+       # any communication
+       comm.subset <- comm.any[members, members]  # comm. partners in this coal.
+       
+       groupi <- which(rownames(comm.subset) == rownames(mem)[i])
+       indeg.coal <- sum(comm.subset[groupi, ])  # indegree of group i in coal.
+       commpart.indeg.any[i, j] <- indeg.coal / (sum(mem[, j]) - 1)
+       
+       outdeg.coal <- sum(comm.subset[, groupi])  # outdegree of group i in coal.
+       commpart.outdeg.any[i, j] <- outdeg.coal / (sum(mem[, j]) - 1)
+       
+       # regular communication
+       comm.subset <- comm.reg[members, members]  # comm. partners in this coal.
+       
+       groupi <- which(rownames(comm.subset) == rownames(mem)[i])
+       indeg.coal <- sum(comm.subset[groupi, ])  # indegree of group i in coal.
+       commpart.indeg.reg[i, j] <- indeg.coal / (sum(mem[, j]) - 1)
+       
+       outdeg.coal <- sum(comm.subset[, groupi])  # outdegree of group i in coal.
+       commpart.outdeg.reg[i, j] <- outdeg.coal / (sum(mem[, j]) - 1)
+     }
+   }
+ }
> 
> # Interest_Group_Coalition_Partisan_Differential: absolute difference in 
> # conservatism group vs. coalition
> # set the node attribute for re-use with the absdiff term
> set.vertex.attribute(leader, "Partisanship", 
+     attrib$Conservative_Lean_of_Organization_Coalition)
> Interest_Group_Coalition_Partisan_Differential <- matrix(NA, 
+     nrow = nrow(as.matrix(leader)), ncol = ncol(as.matrix(leader)))
> cl <- attrib$Conservative_Lean_of_Organization_Coalition
> cl.ig <- cl[1:nrow(as.matrix(leader))]
> cl.coal <- cl[(nrow(as.matrix(leader)) + 1):length(cl)]
> for (i in 1:length(cl.ig)) {
+   for (j in 1:length(cl.coal)) {
+     Interest_Group_Coalition_Partisan_Differential[i, j] <- abs(cl.ig[i] - 
+         cl.coal[j])
+   }
+ }
> 
> # Interest_Group_Partisanship: conservatism of the group
> Interest_Group_Partisanship <- matrix(NA, nrow = nrow(mem), ncol = ncol(mem))
> for (i in 1:ncol(mem)) {
+   Interest_Group_Partisanship[, i] <- 
+       attrib.grp$Conservative_Lean_of_Organization_Coalition
+ }
> 
> # Coalition_Partisanship: conservatism of the coalition
> Coalition_Partisanship <- matrix(NA, nrow = nrow(mem), ncol = ncol(mem))
> for (i in 1:nrow(mem)) {
+   Coalition_Partisanship[i, ] <- 
+       attrib.coal$Conservative_Lean_of_Organization_Coalition
+ }
> 
> # Lobbying_Expenditures: lobbying expenditure of organization
> Lobbying_Expenditures <- matrix(NA, nrow = nrow(mem), ncol = ncol(mem))
> for (i in 1:ncol(mem)) {
+   Lobbying_Expenditures[, i] <- attrib.grp$Lobbying_Spending_by_Organization
+ }
> 
> # Coalition_Dues: does the coalition collect dues?
> Coalition_Dues <- matrix(NA, nrow = nrow(mem), ncol = ncol(mem))
> for (i in 1:nrow(mem)) {
+   Coalition_Dues[i, ] <- attrib.coal$Coalition_Collects_Dues
+ }
> 
> # Coalition_Faces_Legislative_Threat: coalition responding to legislative threat
> Coalition_Faces_Legislative_Threat <- matrix(NA, nrow = nrow(mem), 
+     ncol = ncol(mem))
> for (i in 1:nrow(mem)) {
+   Coalition_Faces_Legislative_Threat[i, ] <- 
+       attrib.coal$Coalition_Responding_to_Legislative_Threat
+ }
> 
> # Coalition_Focuses_on_Authorizing_Legislation
> Coalition_Focuses_on_Authorizing_Legislation <- matrix(NA, nrow = nrow(mem), 
+     ncol = ncol(mem))
> for (i in 1:nrow(mem)) {
+   Coalition_Focuses_on_Authorizing_Legislation[i, ] <- 
+       attrib.coal$Coalition_Focuses_on_Authorizing_Legislation
+ }
> 
> # Interest_Group_Crosses_Issue_Boundary: 
> # organization primarily active outside health domain
> Interest_Group_Crosses_Issue_Boundary <- matrix(NA, nrow = nrow(mem), 
+     ncol = ncol(mem))
> for (i in 1:ncol(mem)) {
+   Interest_Group_Crosses_Issue_Boundary[, i] <- 
+       attrib.grp$Organization_Identified_Primarily_Outside_Health
+ }
> 
> # Citizens_Advocacy_Group: organization is citizens' advocacy organization
> Citizens_Advocacy_Group <- matrix(NA, nrow = nrow(mem), ncol = ncol(mem))
> for (i in 1:ncol(mem)) {
+   Citizens_Advocacy_Group[, i] <- 
+       attrib.grp$Organization_is_Citizens_Advocacy_Organization
+ }
> 
> # Coalition_Steering_Committee: coalition has a steering committee
> Coalition_Steering_Committee <- matrix(NA, nrow = nrow(mem), ncol = ncol(mem))
> for (i in 1:nrow(mem)) {
+   Coalition_Steering_Committee[i, ] <- 
+       attrib.coal$Coalition_Has_Steering_Committee
+ }
> 
> # Interest_Group_Age: centuries since organization was founded
> Interest_Group_Age <- matrix(NA, nrow = nrow(mem), ncol = ncol(mem))
> for (i in 1:ncol(mem)) {
+   Interest_Group_Age[, i] <- 0.01 * 
+       attrib.grp$Years_Since_Founding_of_Organization_Coalition
+ }
> 
> # Coalition_Age: centuries since coalition was founded
> Coalition_Age <- matrix(NA, nrow = nrow(mem), ncol = ncol(mem))
> for (i in 1:nrow(mem)) {
+   Coalition_Age[i, ] <- 0.01 * 
+       attrib.coal$Years_Since_Founding_of_Organization_Coalition
+ }
> 
> 
> # ==============================================================================
> # Estimate ERGMs
> # ==============================================================================
> 
> # model 1: non-members as structural zeros
> model.1 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(Network_Embeddedness_Strong)
+     # controls
+     + b1star(2)
+     + edgecov(Visibility)
+     + edgecov(Controversial)
+     + edgecov(Interest_Group_Coalition_Partisan_Differential)
+     + edgecov(Number_of_Coalition_Memberships)
+     + edgecov(Coalition_Size)
+     + edgecov(Interest_Group_Partisanship)
+     + edgecov(Coalition_Partisanship)
+     + edgecov(Interest_Group_Age)
+     + edgecov(Coalition_Age)
+     + edgecov(Citizens_Advocacy_Group)
+     + edgecov(Coalition_Dues)
+     + edgecov(Lobbying_Expenditures)
+     + edgecov(Interest_Group_Crosses_Issue_Boundary)
+     + edgecov(Coalition_Faces_Legislative_Threat)
+     + edgecov(Coalition_Focuses_on_Authorizing_Legislation)
+     + edgecov(Coalition_Steering_Committee)
+     + offset(edgecov(nonmem)), 
+     offset.coef = -Inf, eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 0.4817 
Step length converged once. Increasing MCMC sample size.
Iteration 2 of at most 200: 
The log-likelihood improved by 0.05101 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> # model 2: non-members as structural zeros, founders who are leaders as 
> # structural ones
> model.2 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(Network_Embeddedness_Strong)
+     # controls
+     + b1star(2)
+     + edgecov(Visibility)
+     + edgecov(Controversial)
+     + edgecov(Interest_Group_Coalition_Partisan_Differential)
+     + edgecov(Number_of_Coalition_Memberships)
+     + edgecov(Coalition_Size)
+     + edgecov(Interest_Group_Partisanship)
+     + edgecov(Coalition_Partisanship)
+     + edgecov(Interest_Group_Age)
+     + edgecov(Coalition_Age)
+     + edgecov(Citizens_Advocacy_Group)
+     + edgecov(Coalition_Dues)
+     + edgecov(Lobbying_Expenditures)
+     + edgecov(Interest_Group_Crosses_Issue_Boundary)
+     + edgecov(Coalition_Faces_Legislative_Threat)
+     + edgecov(Coalition_Focuses_on_Authorizing_Legislation)
+     + edgecov(Coalition_Steering_Committee)
+     + offset(edgecov(nonmem)) + offset(edgecov(founderleader)), 
+     offset.coef = c(-Inf, Inf), eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 0.2781 
Step length converged once. Increasing MCMC sample size.
Iteration 2 of at most 200: 
The log-likelihood improved by 0.0289 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> # model 3: same as model 1, but with diversity x embeddedness interaction
> model.3 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(Network_Embeddedness_Strong)
+     + edgecov(Diversity_X_Embeddedness_Strong)
+     # controls
+     + b1star(2)
+     + edgecov(Visibility)
+     + edgecov(Controversial)
+     + edgecov(Interest_Group_Coalition_Partisan_Differential)
+     + edgecov(Number_of_Coalition_Memberships)
+     + edgecov(Coalition_Size)
+     + edgecov(Interest_Group_Partisanship)
+     + edgecov(Coalition_Partisanship)
+     + edgecov(Interest_Group_Age)
+     + edgecov(Coalition_Age)
+     + edgecov(Citizens_Advocacy_Group)
+     + edgecov(Coalition_Dues)
+     + edgecov(Lobbying_Expenditures)
+     + edgecov(Interest_Group_Crosses_Issue_Boundary)
+     + edgecov(Coalition_Faces_Legislative_Threat)
+     + edgecov(Coalition_Focuses_on_Authorizing_Legislation)
+     + edgecov(Coalition_Steering_Committee)
+     + offset(edgecov(nonmem)), 
+     offset.coef = -Inf, eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 0.4432 
Step length converged once. Increasing MCMC sample size.
Iteration 2 of at most 200: 
The log-likelihood improved by 0.03357 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> # model 4: same as model 2, but with diversity x embeddedness interaction
> model.4 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(Network_Embeddedness_Strong)
+     + edgecov(Diversity_X_Embeddedness_Strong)
+     # controls
+     + b1star(2)
+     + edgecov(Visibility)
+     + edgecov(Controversial)
+     + edgecov(Interest_Group_Coalition_Partisan_Differential)
+     + edgecov(Number_of_Coalition_Memberships)
+     + edgecov(Coalition_Size)
+     + edgecov(Interest_Group_Partisanship)
+     + edgecov(Coalition_Partisanship)
+     + edgecov(Interest_Group_Age)
+     + edgecov(Coalition_Age)
+     + edgecov(Citizens_Advocacy_Group)
+     + edgecov(Coalition_Dues)
+     + edgecov(Lobbying_Expenditures)
+     + edgecov(Interest_Group_Crosses_Issue_Boundary)
+     + edgecov(Coalition_Faces_Legislative_Threat)
+     + edgecov(Coalition_Focuses_on_Authorizing_Legislation)
+     + edgecov(Coalition_Steering_Committee)
+     + offset(edgecov(nonmem)) + offset(edgecov(founderleader)), 
+     offset.coef = c(-Inf, Inf), eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 0.2306 
Step length converged once. Increasing MCMC sample size.
Iteration 2 of at most 200: 
The log-likelihood improved by 0.0137 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> # models in the main manuscript
> htmlreg(list(model.1, model.2, model.3, model.4), single.row = TRUE, 
+     file = "Models 1-4.html")
The table was written to the file 'Models 1-4.html'.

> 
> 
> # model 5: non-members and founders as structural zeros
> model.5 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(Network_Embeddedness_Strong)
+     # controls
+     + b1star(2)
+     + edgecov(Visibility)
+     + edgecov(Controversial)
+     + edgecov(Interest_Group_Coalition_Partisan_Differential)
+     + edgecov(Number_of_Coalition_Memberships)
+     + edgecov(Coalition_Size)
+     + edgecov(Interest_Group_Partisanship)
+     + edgecov(Coalition_Partisanship)
+     + edgecov(Interest_Group_Age)
+     + edgecov(Coalition_Age)
+     + edgecov(Citizens_Advocacy_Group)
+     + edgecov(Coalition_Dues)
+     + edgecov(Lobbying_Expenditures)
+     + edgecov(Interest_Group_Crosses_Issue_Boundary)
+     + edgecov(Coalition_Faces_Legislative_Threat)
+     + edgecov(Coalition_Focuses_on_Authorizing_Legislation)
+     + edgecov(Coalition_Steering_Committee)
+     + offset(edgecov(nonmem2)), 
+     offset.coef = -Inf, eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 5.36 
Iteration 2 of at most 200: 
The log-likelihood improved by 4.15 
Iteration 3 of at most 200: 
The log-likelihood improved by 3.173 
Iteration 4 of at most 200: 
The log-likelihood improved by 3.503 
Iteration 5 of at most 200: 
The log-likelihood improved by 2.478 
Step length converged once. Increasing MCMC sample size.
Iteration 6 of at most 200: 
The log-likelihood improved by 0.4626 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> # model 6: no structural zeros at all
> model.6 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(Network_Embeddedness_Strong)
+     # controls
+     + b1star(2)
+     + edgecov(Visibility)
+     + edgecov(Controversial)
+     + edgecov(Interest_Group_Coalition_Partisan_Differential)
+     + edgecov(Number_of_Coalition_Memberships)
+     + edgecov(Coalition_Size)
+     + edgecov(Interest_Group_Partisanship)
+     + edgecov(Coalition_Partisanship)
+     + edgecov(Interest_Group_Age)
+     + edgecov(Coalition_Age)
+     + edgecov(Citizens_Advocacy_Group)
+     + edgecov(Coalition_Dues)
+     + edgecov(Lobbying_Expenditures)
+     + edgecov(Interest_Group_Crosses_Issue_Boundary)
+     + edgecov(Coalition_Faces_Legislative_Threat)
+     + edgecov(Coalition_Focuses_on_Authorizing_Legislation)
+     + edgecov(Coalition_Steering_Committee)
+     , eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 1.333 
Step length converged once. Increasing MCMC sample size.
Iteration 2 of at most 200: 
The log-likelihood improved by 0.1551 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> # model 7: substituting strong for weak embeddedness in model 1
> model.7 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(Network_Embeddedness_Weak)
+     # controls
+     + b1star(2)
+     + edgecov(Visibility)
+     + edgecov(Controversial)
+     + edgecov(Interest_Group_Coalition_Partisan_Differential)
+     + edgecov(Number_of_Coalition_Memberships)
+     + edgecov(Coalition_Size)
+     + edgecov(Interest_Group_Partisanship)
+     + edgecov(Coalition_Partisanship)
+     + edgecov(Interest_Group_Age)
+     + edgecov(Coalition_Age)
+     + edgecov(Citizens_Advocacy_Group)
+     + edgecov(Coalition_Dues)
+     + edgecov(Lobbying_Expenditures)
+     + edgecov(Interest_Group_Crosses_Issue_Boundary)
+     + edgecov(Coalition_Faces_Legislative_Threat)
+     + edgecov(Coalition_Focuses_on_Authorizing_Legislation)
+     + edgecov(Coalition_Steering_Committee)
+     + offset(edgecov(nonmem)), 
+     offset.coef = -Inf, eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 0.6104 
Step length converged once. Increasing MCMC sample size.
Iteration 2 of at most 200: 
The log-likelihood improved by 0.04198 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> # model 8: substituting strong for weak embeddedness in model 3
> model.8 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(Network_Embeddedness_Weak)
+     + edgecov(Diversity_X_Embeddedness_Weak)
+     # controls
+     + b1star(2)
+     + edgecov(Visibility)
+     + edgecov(Controversial)
+     + edgecov(Interest_Group_Coalition_Partisan_Differential)
+     + edgecov(Number_of_Coalition_Memberships)
+     + edgecov(Coalition_Size)
+     + edgecov(Interest_Group_Partisanship)
+     + edgecov(Coalition_Partisanship)
+     + edgecov(Interest_Group_Age)
+     + edgecov(Coalition_Age)
+     + edgecov(Citizens_Advocacy_Group)
+     + edgecov(Coalition_Dues)
+     + edgecov(Lobbying_Expenditures)
+     + edgecov(Interest_Group_Crosses_Issue_Boundary)
+     + edgecov(Coalition_Faces_Legislative_Threat)
+     + edgecov(Coalition_Focuses_on_Authorizing_Legislation)
+     + edgecov(Coalition_Steering_Committee)
+     + offset(edgecov(nonmem)), 
+     offset.coef = -Inf, eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 0.6694 
Step length converged once. Increasing MCMC sample size.
Iteration 2 of at most 200: 
The log-likelihood improved by 0.03496 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> htmlreg(list(model.5, model.6, model.7, model.8), single.row = TRUE, 
+     custom.model.names = paste("Model", 5:8), file = "Models 5-8.html")
The table was written to the file 'Models 5-8.html'.

> 
> # model 9: like model 1, but using communication density instead of network 
> # embeddedness
> model.9 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(commdensity)
+     # controls
+     + b1star(2)
+     + edgecov(Visibility)
+     + edgecov(Controversial)
+     + edgecov(Interest_Group_Coalition_Partisan_Differential)
+     + edgecov(Number_of_Coalition_Memberships)
+     + edgecov(Coalition_Size)
+     + edgecov(Interest_Group_Partisanship)
+     + edgecov(Coalition_Partisanship)
+     + edgecov(Interest_Group_Age)
+     + edgecov(Coalition_Age)
+     + edgecov(Citizens_Advocacy_Group)
+     + edgecov(Coalition_Dues)
+     + edgecov(Lobbying_Expenditures)
+     + edgecov(Interest_Group_Crosses_Issue_Boundary)
+     + edgecov(Coalition_Faces_Legislative_Threat)
+     + edgecov(Coalition_Focuses_on_Authorizing_Legislation)
+     + edgecov(Coalition_Steering_Committee)
+     + offset(edgecov(nonmem)), 
+     offset.coef = -Inf, eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 0.5716 
Step length converged once. Increasing MCMC sample size.
Iteration 2 of at most 200: 
The log-likelihood improved by 0.05956 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> # model 10: like model 3, but using communication density instead of network 
> # embeddedness
> model.10 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(commdensity)
+     + edgecov(commdensity.diversity)
+     # controls
+     + b1star(2)
+     + edgecov(Visibility)
+     + edgecov(Controversial)
+     + edgecov(Interest_Group_Coalition_Partisan_Differential)
+     + edgecov(Number_of_Coalition_Memberships)
+     + edgecov(Coalition_Size)
+     + edgecov(Interest_Group_Partisanship)
+     + edgecov(Coalition_Partisanship)
+     + edgecov(Interest_Group_Age)
+     + edgecov(Coalition_Age)
+     + edgecov(Citizens_Advocacy_Group)
+     + edgecov(Coalition_Dues)
+     + edgecov(Lobbying_Expenditures)
+     + edgecov(Interest_Group_Crosses_Issue_Boundary)
+     + edgecov(Coalition_Faces_Legislative_Threat)
+     + edgecov(Coalition_Focuses_on_Authorizing_Legislation)
+     + edgecov(Coalition_Steering_Committee)
+     + offset(edgecov(nonmem)), 
+     offset.coef = -Inf, eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 0.5497 
Step length converged once. Increasing MCMC sample size.
Iteration 2 of at most 200: 
The log-likelihood improved by 0.04243 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> # model 11: network embeddedness only in the same party (within-party ties)
> model.11 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(Network_Embeddedness_Strong_SameParty)
+     # controls
+     + b1star(2)
+     + edgecov(Visibility)
+     + edgecov(Controversial)
+     + edgecov(Interest_Group_Coalition_Partisan_Differential)
+     + edgecov(Number_of_Coalition_Memberships)
+     + edgecov(Coalition_Size)
+     + edgecov(Interest_Group_Partisanship)
+     + edgecov(Coalition_Partisanship)
+     + edgecov(Interest_Group_Age)
+     + edgecov(Coalition_Age)
+     + edgecov(Citizens_Advocacy_Group)
+     + edgecov(Coalition_Dues)
+     + edgecov(Lobbying_Expenditures)
+     + edgecov(Interest_Group_Crosses_Issue_Boundary)
+     + edgecov(Coalition_Faces_Legislative_Threat)
+     + edgecov(Coalition_Focuses_on_Authorizing_Legislation)
+     + edgecov(Coalition_Steering_Committee)
+     + offset(edgecov(nonmem)), 
+     offset.coef = -Inf, eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 0.6666 
Step length converged once. Increasing MCMC sample size.
Iteration 2 of at most 200: 
The log-likelihood improved by 0.04365 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> # model 12: network embeddedness only across parties (cross-party ties)
> model.12 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(Network_Embeddedness_Strong_CrossParty)
+     # controls
+     + b1star(2)
+     + edgecov(Visibility)
+     + edgecov(Controversial)
+     + edgecov(Interest_Group_Coalition_Partisan_Differential)
+     + edgecov(Number_of_Coalition_Memberships)
+     + edgecov(Coalition_Size)
+     + edgecov(Interest_Group_Partisanship)
+     + edgecov(Coalition_Partisanship)
+     + edgecov(Interest_Group_Age)
+     + edgecov(Coalition_Age)
+     + edgecov(Citizens_Advocacy_Group)
+     + edgecov(Coalition_Dues)
+     + edgecov(Lobbying_Expenditures)
+     + edgecov(Interest_Group_Crosses_Issue_Boundary)
+     + edgecov(Coalition_Faces_Legislative_Threat)
+     + edgecov(Coalition_Focuses_on_Authorizing_Legislation)
+     + edgecov(Coalition_Steering_Committee)
+     + offset(edgecov(nonmem)), 
+     offset.coef = -Inf, eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 0.4352 
Step length converged once. Increasing MCMC sample size.
Iteration 2 of at most 200: 
The log-likelihood improved by 0.03627 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> htmlreg(list(model.9, model.10, model.11, model.12), single.row = TRUE, 
+     custom.model.names = paste("Model", 9:12), file = "Models 9-12.html")
The table was written to the file 'Models 9-12.html'.

> 
> # model 13: only main effects and endogenous model terms
> model.13 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(Network_Embeddedness_Strong)
+     # controls
+     + b1star(2)
+     + offset(edgecov(nonmem)), 
+     offset.coef = -Inf, eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 1.687 
Step length converged once. Increasing MCMC sample size.
Iteration 2 of at most 200: 
The log-likelihood improved by 0.1845 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> # model 14: like model 13, but with number of coalition memberships
> model.14 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(Network_Embeddedness_Strong)
+     # controls
+     + b1star(2)
+     + edgecov(Number_of_Coalition_Memberships)
+     + offset(edgecov(nonmem)), 
+     offset.coef = -Inf, eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 0.8955 
Step length converged once. Increasing MCMC sample size.
Iteration 2 of at most 200: 
The log-likelihood improved by 0.1078 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> # model 15: add five other diversity variables to model 1
> model.15 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(Network_Embeddedness_Strong)
+     + edgecov(diversity.age)
+     + edgecov(diversity.lobspend)
+     + edgecov(diversity.citadv)
+     + edgecov(diversity.outshealth)
+     # controls
+     + b1star(2)
+     + edgecov(Visibility)
+     + edgecov(Controversial)
+     + edgecov(Interest_Group_Coalition_Partisan_Differential)
+     + edgecov(Number_of_Coalition_Memberships)
+     + edgecov(Coalition_Size)
+     + edgecov(Interest_Group_Partisanship)
+     + edgecov(Coalition_Partisanship)
+     + edgecov(Interest_Group_Age)
+     + edgecov(Coalition_Age)
+     + edgecov(Citizens_Advocacy_Group)
+     + edgecov(Coalition_Dues)
+     + edgecov(Lobbying_Expenditures)
+     + edgecov(Interest_Group_Crosses_Issue_Boundary)
+     + edgecov(Coalition_Faces_Legislative_Threat)
+     + edgecov(Coalition_Focuses_on_Authorizing_Legislation)
+     + edgecov(Coalition_Steering_Committee)
+     + offset(edgecov(nonmem)), 
+     offset.coef = -Inf, eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 0.5452 
Step length converged once. Increasing MCMC sample size.
Iteration 2 of at most 200: 
The log-likelihood improved by 0.06652 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> # model 16: like model 1, but interaction between visibility and diversity
> model.16 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(Network_Embeddedness_Strong)
+     # controls
+     + b1star(2)
+     + edgecov(Visibility)
+     + edgecov(Diversity_X_Visibility)
+     + edgecov(Controversial)
+     + edgecov(Interest_Group_Coalition_Partisan_Differential)
+     + edgecov(Number_of_Coalition_Memberships)
+     + edgecov(Coalition_Size)
+     + edgecov(Interest_Group_Partisanship)
+     + edgecov(Coalition_Partisanship)
+     + edgecov(Interest_Group_Age)
+     + edgecov(Coalition_Age)
+     + edgecov(Citizens_Advocacy_Group)
+     + edgecov(Coalition_Dues)
+     + edgecov(Lobbying_Expenditures)
+     + edgecov(Interest_Group_Crosses_Issue_Boundary)
+     + edgecov(Coalition_Faces_Legislative_Threat)
+     + edgecov(Coalition_Focuses_on_Authorizing_Legislation)
+     + edgecov(Coalition_Steering_Committee)
+     + offset(edgecov(nonmem)), 
+     offset.coef = -Inf, eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 0.5612 
Step length converged once. Increasing MCMC sample size.
Iteration 2 of at most 200: 
The log-likelihood improved by 0.05185 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> htmlreg(list(model.13, model.14, model.15, model.16), single.row = TRUE, 
+     custom.model.names = paste("Model", 13:16), file = "Models 13-16.html")
The table was written to the file 'Models 13-16.html'.

> 
> # model 17: like model 1, but interaction between controversial and diversity
> model.17 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(Network_Embeddedness_Strong)
+     # controls
+     + b1star(2)
+     + edgecov(Visibility)
+     + edgecov(Diversity_X_Controversial)
+     + edgecov(Controversial)
+     + edgecov(Interest_Group_Coalition_Partisan_Differential)
+     + edgecov(Number_of_Coalition_Memberships)
+     + edgecov(Coalition_Size)
+     + edgecov(Interest_Group_Partisanship)
+     + edgecov(Coalition_Partisanship)
+     + edgecov(Interest_Group_Age)
+     + edgecov(Coalition_Age)
+     + edgecov(Citizens_Advocacy_Group)
+     + edgecov(Coalition_Dues)
+     + edgecov(Lobbying_Expenditures)
+     + edgecov(Interest_Group_Crosses_Issue_Boundary)
+     + edgecov(Coalition_Faces_Legislative_Threat)
+     + edgecov(Coalition_Focuses_on_Authorizing_Legislation)
+     + edgecov(Coalition_Steering_Committee)
+     + offset(edgecov(nonmem)), 
+     offset.coef = -Inf, eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 0.5626 
Step length converged once. Increasing MCMC sample size.
Iteration 2 of at most 200: 
The log-likelihood improved by 0.03741 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> # model 18: coalition two-stars instead of number of coalition memberships and 
> # coalition size (as these are collinear)
> model.18 <- ergm(
+     leader ~ 
+     + edges
+     # main effects
+     + edgecov(Partisan_Diversity)
+     + edgecov(Network_Embeddedness_Strong)
+     # controls
+     + b1star(2)
+     + b2star(2)
+     + edgecov(Visibility)
+     + edgecov(Controversial)
+     + edgecov(Interest_Group_Coalition_Partisan_Differential)
+     + edgecov(Interest_Group_Partisanship)
+     + edgecov(Coalition_Partisanship)
+     + edgecov(Interest_Group_Age)
+     + edgecov(Coalition_Age)
+     + edgecov(Citizens_Advocacy_Group)
+     + edgecov(Coalition_Dues)
+     + edgecov(Lobbying_Expenditures)
+     + edgecov(Interest_Group_Crosses_Issue_Boundary)
+     + edgecov(Coalition_Faces_Legislative_Threat)
+     + edgecov(Coalition_Focuses_on_Authorizing_Legislation)
+     + edgecov(Coalition_Steering_Committee)
+     + offset(edgecov(nonmem)), 
+     offset.coef = -Inf, eval.loglik = FALSE, 
+     control = control.ergm(MCMC.burnin = burnin, MCMC.samplesize = sampsize, 
+         seed = seed, MCMLE.maxit = maxit)
+ )
Starting maximum likelihood estimation via MCMLE:
Iteration 1 of at most 200: 
The log-likelihood improved by 2.504 
Iteration 2 of at most 200: 
The log-likelihood improved by 2.245 
Step length converged once. Increasing MCMC sample size.
Iteration 3 of at most 200: 
The log-likelihood improved by 0.5103 
Step length converged twice. Stopping.

This model was fit using MCMC.  To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
> 
> htmlreg(list(model.17, model.18), single.row = TRUE,  custom.model.names =
+     paste("Model", 17:18), file = "Models 17-18.html")
The table was written to the file 'Models 17-18.html'.

> 
> 
> # ==============================================================================
> # Assess goodness of fit
> # ==============================================================================
> 
> # boxplot diagrams
> mygof <- function(model, number) {
+   gf <- gof(model, nsim = nsim, statistics = c(nsp, b1deg, b2deg, geodesic, 
+       b1star, b2star, rocpr), ncpus = cores, parallel = "multicore", 
+       roc = FALSE)
+   temp <- gf[1:6]
+   class(temp) <- "gof"
+   pdf(paste0("gof.", number, ".pdf"), width = 9, height = 6)
+   plot(temp)
+   dev.off()
+   return(gf)
+ }
> 
> gof.1 <- mygof(model.1, 1)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(Network_Embeddedness_Strong) + b1star(2) + edgecov(Visibility) + edgecov(Controversial) + edgecov(Interest_Group_Coalition_Partisan_Differential) + edgecov(Number_of_Coalition_Memberships) + edgecov(Coalition_Size) + edgecov(Interest_Group_Partisanship) + edgecov(Coalition_Partisanship) + edgecov(Interest_Group_Age) + edgecov(Coalition_Age) + edgecov(Citizens_Advocacy_Group) + edgecov(Coalition_Dues) + edgecov(Lobbying_Expenditures) + edgecov(Interest_Group_Crosses_Issue_Boundary) + edgecov(Coalition_Faces_Legislative_Threat) + edgecov(Coalition_Focuses_on_Authorizing_Legislation) + edgecov(Coalition_Steering_Committee) + offset(edgecov(nonmem)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.2, 2)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(Network_Embeddedness_Strong) + b1star(2) + edgecov(Visibility) + edgecov(Controversial) + edgecov(Interest_Group_Coalition_Partisan_Differential) + edgecov(Number_of_Coalition_Memberships) + edgecov(Coalition_Size) + edgecov(Interest_Group_Partisanship) + edgecov(Coalition_Partisanship) + edgecov(Interest_Group_Age) + edgecov(Coalition_Age) + edgecov(Citizens_Advocacy_Group) + edgecov(Coalition_Dues) + edgecov(Lobbying_Expenditures) + edgecov(Interest_Group_Crosses_Issue_Boundary) + edgecov(Coalition_Faces_Legislative_Threat) + edgecov(Coalition_Focuses_on_Authorizing_Legislation) + edgecov(Coalition_Steering_Committee) + offset(edgecov(nonmem)) + offset(edgecov(founderleader)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.3, 3)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(Network_Embeddedness_Strong) + edgecov(Diversity_X_Embeddedness_Strong) + b1star(2) + edgecov(Visibility) + edgecov(Controversial) + edgecov(Interest_Group_Coalition_Partisan_Differential) + edgecov(Number_of_Coalition_Memberships) + edgecov(Coalition_Size) + edgecov(Interest_Group_Partisanship) + edgecov(Coalition_Partisanship) + edgecov(Interest_Group_Age) + edgecov(Coalition_Age) + edgecov(Citizens_Advocacy_Group) + edgecov(Coalition_Dues) + edgecov(Lobbying_Expenditures) + edgecov(Interest_Group_Crosses_Issue_Boundary) + edgecov(Coalition_Faces_Legislative_Threat) + edgecov(Coalition_Focuses_on_Authorizing_Legislation) + edgecov(Coalition_Steering_Committee) + offset(edgecov(nonmem)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.4, 4)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(Network_Embeddedness_Strong) + edgecov(Diversity_X_Embeddedness_Strong) + b1star(2) + edgecov(Visibility) + edgecov(Controversial) + edgecov(Interest_Group_Coalition_Partisan_Differential) + edgecov(Number_of_Coalition_Memberships) + edgecov(Coalition_Size) + edgecov(Interest_Group_Partisanship) + edgecov(Coalition_Partisanship) + edgecov(Interest_Group_Age) + edgecov(Coalition_Age) + edgecov(Citizens_Advocacy_Group) + edgecov(Coalition_Dues) + edgecov(Lobbying_Expenditures) + edgecov(Interest_Group_Crosses_Issue_Boundary) + edgecov(Coalition_Faces_Legislative_Threat) + edgecov(Coalition_Focuses_on_Authorizing_Legislation) + edgecov(Coalition_Steering_Committee) + offset(edgecov(nonmem)) + offset(edgecov(founderleader)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.5, 5)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(Network_Embeddedness_Strong) + b1star(2) + edgecov(Visibility) + edgecov(Controversial) + edgecov(Interest_Group_Coalition_Partisan_Differential) + edgecov(Number_of_Coalition_Memberships) + edgecov(Coalition_Size) + edgecov(Interest_Group_Partisanship) + edgecov(Coalition_Partisanship) + edgecov(Interest_Group_Age) + edgecov(Coalition_Age) + edgecov(Citizens_Advocacy_Group) + edgecov(Coalition_Dues) + edgecov(Lobbying_Expenditures) + edgecov(Interest_Group_Crosses_Issue_Boundary) + edgecov(Coalition_Faces_Legislative_Threat) + edgecov(Coalition_Focuses_on_Authorizing_Legislation) + edgecov(Coalition_Steering_Committee) + offset(edgecov(nonmem2)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.6, 6)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(Network_Embeddedness_Strong) + b1star(2) + edgecov(Visibility) + edgecov(Controversial) + edgecov(Interest_Group_Coalition_Partisan_Differential) + edgecov(Number_of_Coalition_Memberships) + edgecov(Coalition_Size) + edgecov(Interest_Group_Partisanship) + edgecov(Coalition_Partisanship) + edgecov(Interest_Group_Age) + edgecov(Coalition_Age) + edgecov(Citizens_Advocacy_Group) + edgecov(Coalition_Dues) + edgecov(Lobbying_Expenditures) + edgecov(Interest_Group_Crosses_Issue_Boundary) + edgecov(Coalition_Faces_Legislative_Threat) + edgecov(Coalition_Focuses_on_Authorizing_Legislation) + edgecov(Coalition_Steering_Committee) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.7, 7)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(Network_Embeddedness_Weak) + b1star(2) + edgecov(Visibility) + edgecov(Controversial) + edgecov(Interest_Group_Coalition_Partisan_Differential) + edgecov(Number_of_Coalition_Memberships) + edgecov(Coalition_Size) + edgecov(Interest_Group_Partisanship) + edgecov(Coalition_Partisanship) + edgecov(Interest_Group_Age) + edgecov(Coalition_Age) + edgecov(Citizens_Advocacy_Group) + edgecov(Coalition_Dues) + edgecov(Lobbying_Expenditures) + edgecov(Interest_Group_Crosses_Issue_Boundary) + edgecov(Coalition_Faces_Legislative_Threat) + edgecov(Coalition_Focuses_on_Authorizing_Legislation) + edgecov(Coalition_Steering_Committee) + offset(edgecov(nonmem)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.8, 8)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(Network_Embeddedness_Weak) + edgecov(Diversity_X_Embeddedness_Weak) + b1star(2) + edgecov(Visibility) + edgecov(Controversial) + edgecov(Interest_Group_Coalition_Partisan_Differential) + edgecov(Number_of_Coalition_Memberships) + edgecov(Coalition_Size) + edgecov(Interest_Group_Partisanship) + edgecov(Coalition_Partisanship) + edgecov(Interest_Group_Age) + edgecov(Coalition_Age) + edgecov(Citizens_Advocacy_Group) + edgecov(Coalition_Dues) + edgecov(Lobbying_Expenditures) + edgecov(Interest_Group_Crosses_Issue_Boundary) + edgecov(Coalition_Faces_Legislative_Threat) + edgecov(Coalition_Focuses_on_Authorizing_Legislation) + edgecov(Coalition_Steering_Committee) + offset(edgecov(nonmem)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.9, 9)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(commdensity) + b1star(2) + edgecov(Visibility) + edgecov(Controversial) + edgecov(Interest_Group_Coalition_Partisan_Differential) + edgecov(Number_of_Coalition_Memberships) + edgecov(Coalition_Size) + edgecov(Interest_Group_Partisanship) + edgecov(Coalition_Partisanship) + edgecov(Interest_Group_Age) + edgecov(Coalition_Age) + edgecov(Citizens_Advocacy_Group) + edgecov(Coalition_Dues) + edgecov(Lobbying_Expenditures) + edgecov(Interest_Group_Crosses_Issue_Boundary) + edgecov(Coalition_Faces_Legislative_Threat) + edgecov(Coalition_Focuses_on_Authorizing_Legislation) + edgecov(Coalition_Steering_Committee) + offset(edgecov(nonmem)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.10, 10)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(commdensity) + edgecov(commdensity.diversity) + b1star(2) + edgecov(Visibility) + edgecov(Controversial) + edgecov(Interest_Group_Coalition_Partisan_Differential) + edgecov(Number_of_Coalition_Memberships) + edgecov(Coalition_Size) + edgecov(Interest_Group_Partisanship) + edgecov(Coalition_Partisanship) + edgecov(Interest_Group_Age) + edgecov(Coalition_Age) + edgecov(Citizens_Advocacy_Group) + edgecov(Coalition_Dues) + edgecov(Lobbying_Expenditures) + edgecov(Interest_Group_Crosses_Issue_Boundary) + edgecov(Coalition_Faces_Legislative_Threat) + edgecov(Coalition_Focuses_on_Authorizing_Legislation) + edgecov(Coalition_Steering_Committee) + offset(edgecov(nonmem)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.11, 11)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(Network_Embeddedness_Strong_SameParty) + b1star(2) + edgecov(Visibility) + edgecov(Controversial) + edgecov(Interest_Group_Coalition_Partisan_Differential) + edgecov(Number_of_Coalition_Memberships) + edgecov(Coalition_Size) + edgecov(Interest_Group_Partisanship) + edgecov(Coalition_Partisanship) + edgecov(Interest_Group_Age) + edgecov(Coalition_Age) + edgecov(Citizens_Advocacy_Group) + edgecov(Coalition_Dues) + edgecov(Lobbying_Expenditures) + edgecov(Interest_Group_Crosses_Issue_Boundary) + edgecov(Coalition_Faces_Legislative_Threat) + edgecov(Coalition_Focuses_on_Authorizing_Legislation) + edgecov(Coalition_Steering_Committee) + offset(edgecov(nonmem)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.12, 12)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(Network_Embeddedness_Strong_CrossParty) + b1star(2) + edgecov(Visibility) + edgecov(Controversial) + edgecov(Interest_Group_Coalition_Partisan_Differential) + edgecov(Number_of_Coalition_Memberships) + edgecov(Coalition_Size) + edgecov(Interest_Group_Partisanship) + edgecov(Coalition_Partisanship) + edgecov(Interest_Group_Age) + edgecov(Coalition_Age) + edgecov(Citizens_Advocacy_Group) + edgecov(Coalition_Dues) + edgecov(Lobbying_Expenditures) + edgecov(Interest_Group_Crosses_Issue_Boundary) + edgecov(Coalition_Faces_Legislative_Threat) + edgecov(Coalition_Focuses_on_Authorizing_Legislation) + edgecov(Coalition_Steering_Committee) + offset(edgecov(nonmem)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.13, 13)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(Network_Embeddedness_Strong) + b1star(2) + offset(edgecov(nonmem)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.14, 14)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(Network_Embeddedness_Strong) + b1star(2) + edgecov(Number_of_Coalition_Memberships) + offset(edgecov(nonmem)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.15, 15)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(Network_Embeddedness_Strong) + edgecov(diversity.age) + edgecov(diversity.lobspend) + edgecov(diversity.citadv) + edgecov(diversity.outshealth) + b1star(2) + edgecov(Visibility) + edgecov(Controversial) + edgecov(Interest_Group_Coalition_Partisan_Differential) + edgecov(Number_of_Coalition_Memberships) + edgecov(Coalition_Size) + edgecov(Interest_Group_Partisanship) + edgecov(Coalition_Partisanship) + edgecov(Interest_Group_Age) + edgecov(Coalition_Age) + edgecov(Citizens_Advocacy_Group) + edgecov(Coalition_Dues) + edgecov(Lobbying_Expenditures) + edgecov(Interest_Group_Crosses_Issue_Boundary) + edgecov(Coalition_Faces_Legislative_Threat) + edgecov(Coalition_Focuses_on_Authorizing_Legislation) + edgecov(Coalition_Steering_Committee) + offset(edgecov(nonmem)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.16, 16)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(Network_Embeddedness_Strong) + b1star(2) + edgecov(Visibility) + edgecov(Diversity_X_Visibility) + edgecov(Controversial) + edgecov(Interest_Group_Coalition_Partisan_Differential) + edgecov(Number_of_Coalition_Memberships) + edgecov(Coalition_Size) + edgecov(Interest_Group_Partisanship) + edgecov(Coalition_Partisanship) + edgecov(Interest_Group_Age) + edgecov(Coalition_Age) + edgecov(Citizens_Advocacy_Group) + edgecov(Coalition_Dues) + edgecov(Lobbying_Expenditures) + edgecov(Interest_Group_Crosses_Issue_Boundary) + edgecov(Coalition_Faces_Legislative_Threat) + edgecov(Coalition_Focuses_on_Authorizing_Legislation) + edgecov(Coalition_Steering_Committee) + offset(edgecov(nonmem)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.17, 17)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(Network_Embeddedness_Strong) + b1star(2) + edgecov(Visibility) + edgecov(Diversity_X_Controversial) + edgecov(Controversial) + edgecov(Interest_Group_Coalition_Partisan_Differential) + edgecov(Number_of_Coalition_Memberships) + edgecov(Coalition_Size) + edgecov(Interest_Group_Partisanship) + edgecov(Coalition_Partisanship) + edgecov(Interest_Group_Age) + edgecov(Coalition_Age) + edgecov(Citizens_Advocacy_Group) + edgecov(Coalition_Dues) + edgecov(Lobbying_Expenditures) + edgecov(Interest_Group_Crosses_Issue_Boundary) + edgecov(Coalition_Faces_Legislative_Threat) + edgecov(Coalition_Focuses_on_Authorizing_Legislation) + edgecov(Coalition_Steering_Committee) + offset(edgecov(nonmem)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> gof.2 <- mygof(model.18, 18)

Starting GOF assessment using multicore forking on 3 cores....

No 'target' network(s) provided. Using networks on the left-hand side of the model formula as observed networks.

Simulating 1000 networks from the following formula:
 leader ~ +edges + edgecov(Partisan_Diversity) + edgecov(Network_Embeddedness_Strong) + b1star(2) + b2star(2) + edgecov(Visibility) + edgecov(Controversial) + edgecov(Interest_Group_Coalition_Partisan_Differential) + edgecov(Interest_Group_Partisanship) + edgecov(Coalition_Partisanship) + edgecov(Interest_Group_Age) + edgecov(Coalition_Age) + edgecov(Citizens_Advocacy_Group) + edgecov(Coalition_Dues) + edgecov(Lobbying_Expenditures) + edgecov(Interest_Group_Crosses_Issue_Boundary) + edgecov(Coalition_Faces_Legislative_Threat) + edgecov(Coalition_Focuses_on_Authorizing_Legislation) + edgecov(Coalition_Steering_Committee) + offset(edgecov(nonmem)) 

One network from which simulations are drawn was provided.

Processing statistic: Non-edge-wise shared partners
Processing statistic: Degree (first mode)
Processing statistic: Degree (second mode)
Processing statistic: Geodesic distances
Processing statistic: k-star (first mode)
Processing statistic: k-star (second mode)
Processing statistic: Tie prediction
> 
> # precision-recall curves
> gof.1[[7]]$auc.pr
[1] 0.4367357
> gof.2[[7]]$auc.pr
[1] 0.4577988
> pdf("pr.pdf")
> plot(gof.2[[7]], col = "gray50", rgraph = FALSE, lwd = 3, 
+     main = "Precision-recall curves")
> plot(gof.1[[7]], col = "black", rgraph = TRUE, random.col = "gray90", lwd = 3, 
+     add = TRUE)
> legend("topright", legend = c("Model 1", "Model 2", "Random graph"), 
+     col = c("black", "gray50", "gray90"), lty = 1, lwd = 3)
> dev.off()
null device 
          1 
> 
> # MCMC trace plots
> pdf("mcmcdiag.pdf")
> mcmc.diagnostics(model.1)
Sample statistics summary:

Iterations = 10000:40968976
Thinning interval = 1024 
Number of chains = 1 
Sample size per chain = 40000 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

                                                            Mean      SD
edges                                                   0.035175  15.859
edgecov.Partisan_Diversity                              0.197365  70.914
edgecov.Network_Embeddedness_Strong                     0.009268   0.718
b1star2                                                 2.547700  66.120
edgecov.Visibility                                     -0.180550  14.448
edgecov.Controversial                                   0.214400   6.302
edgecov.Interest_Group_Coalition_Partisan_Differential -5.602245  67.718
edgecov.Number_of_Coalition_Memberships                -0.819475 209.598
edgecov.Coalition_Size                                 -4.989525 316.957
edgecov.Interest_Group_Partisanship                    -5.368625  87.898
edgecov.Coalition_Partisanship                         -4.385041  50.954
edgecov.Interest_Group_Age                             -0.059348  13.143
edgecov.Coalition_Age                                  -0.213892   2.584
edgecov.Citizens_Advocacy_Group                         0.421975   7.886
edgecov.Coalition_Dues                                  0.213800   8.804
edgecov.Lobbying_Expenditures                          -9.614979 131.822
edgecov.Interest_Group_Crosses_Issue_Boundary          -0.153200   5.762
edgecov.Coalition_Faces_Legislative_Threat              0.199450   6.372
edgecov.Coalition_Focuses_on_Authorizing_Legislation   -0.176975  11.475
edgecov.Coalition_Steering_Committee                   -0.477900  10.223
                                                       Naive SE Time-series SE
edges                                                   0.07929        0.35531
edgecov.Partisan_Diversity                              0.35457        1.60619
edgecov.Network_Embeddedness_Strong                     0.00359        0.01675
b1star2                                                 0.33060        1.87561
edgecov.Visibility                                      0.07224        0.33277
edgecov.Controversial                                   0.03151        0.12179
edgecov.Interest_Group_Coalition_Partisan_Differential  0.33859        1.37726
edgecov.Number_of_Coalition_Memberships                 1.04799        5.18811
edgecov.Coalition_Size                                  1.58478        6.31186
edgecov.Interest_Group_Partisanship                     0.43949        2.13057
edgecov.Coalition_Partisanship                          0.25477        1.22871
edgecov.Interest_Group_Age                              0.06572        0.33413
edgecov.Coalition_Age                                   0.01292        0.04914
edgecov.Citizens_Advocacy_Group                         0.03943        0.18399
edgecov.Coalition_Dues                                  0.04402        0.18311
edgecov.Lobbying_Expenditures                           0.65911        3.59367
edgecov.Interest_Group_Crosses_Issue_Boundary           0.02881        0.12423
edgecov.Coalition_Faces_Legislative_Threat              0.03186        0.11496
edgecov.Coalition_Focuses_on_Authorizing_Legislation    0.05738        0.24227
edgecov.Coalition_Steering_Committee                    0.05112        0.21318

2. Quantiles for each variable:

                                                           2.5%       25%
edges                                                   -30.000  -11.0000
edgecov.Partisan_Diversity                             -136.159  -47.9265
edgecov.Network_Embeddedness_Strong                      -1.359   -0.4862
b1star2                                                -114.000  -44.0000
edgecov.Visibility                                      -28.000  -10.0000
edgecov.Controversial                                   -12.000   -4.0000
edgecov.Interest_Group_Coalition_Partisan_Differential -137.527  -50.8735
edgecov.Number_of_Coalition_Memberships                -399.000 -146.0000
edgecov.Coalition_Size                                 -604.000 -225.0000
edgecov.Interest_Group_Partisanship                    -179.000  -64.0000
edgecov.Coalition_Partisanship                         -104.105  -38.9983
edgecov.Interest_Group_Age                              -25.170   -9.1300
edgecov.Coalition_Age                                    -5.100   -2.0000
edgecov.Citizens_Advocacy_Group                         -14.000   -5.0000
edgecov.Coalition_Dues                                  -16.000   -6.0000
edgecov.Lobbying_Expenditures                          -259.590 -100.6005
edgecov.Interest_Group_Crosses_Issue_Boundary           -11.000   -4.0000
edgecov.Coalition_Faces_Legislative_Threat              -12.000   -4.0000
edgecov.Coalition_Focuses_on_Authorizing_Legislation    -22.000   -8.0000
edgecov.Coalition_Steering_Committee                    -20.000   -7.0000
                                                              50%      75%
edges                                                    0.000000  11.0000
edgecov.Partisan_Diversity                              -0.844296  47.3601
edgecov.Network_Embeddedness_Strong                     -0.004281   0.4902
b1star2                                                 -1.000000  45.0000
edgecov.Visibility                                       0.000000   9.0000
edgecov.Controversial                                    0.000000   4.0000
edgecov.Interest_Group_Coalition_Partisan_Differential  -7.186519  39.3213
edgecov.Number_of_Coalition_Memberships                 -5.000000 140.0000
edgecov.Coalition_Size                                 -11.000000 206.0000
edgecov.Interest_Group_Partisanship                     -5.000000  54.0000
edgecov.Coalition_Partisanship                          -4.263680  29.8480
edgecov.Interest_Group_Age                              -0.170000   8.7800
edgecov.Coalition_Age                                   -0.275000   1.5100
edgecov.Citizens_Advocacy_Group                          0.000000   6.0000
edgecov.Coalition_Dues                                   0.000000   6.0000
edgecov.Lobbying_Expenditures                          -12.839161  79.0896
edgecov.Interest_Group_Crosses_Issue_Boundary            0.000000   4.0000
edgecov.Coalition_Faces_Legislative_Threat               0.000000   4.0000
edgecov.Coalition_Focuses_on_Authorizing_Legislation     0.000000   7.0000
edgecov.Coalition_Steering_Committee                    -1.000000   6.0000
                                                         97.5%
edges                                                   32.000
edgecov.Partisan_Diversity                             143.182
edgecov.Network_Embeddedness_Strong                      1.445
b1star2                                                142.000
edgecov.Visibility                                      29.000
edgecov.Controversial                                   13.000
edgecov.Interest_Group_Coalition_Partisan_Differential 129.806
edgecov.Number_of_Coalition_Memberships                417.000
edgecov.Coalition_Size                                 636.000
edgecov.Interest_Group_Partisanship                    166.000
edgecov.Coalition_Partisanship                          95.163
edgecov.Interest_Group_Age                              26.230
edgecov.Coalition_Age                                    4.990
edgecov.Citizens_Advocacy_Group                         16.000
edgecov.Coalition_Dues                                  18.000
edgecov.Lobbying_Expenditures                          256.420
edgecov.Interest_Group_Crosses_Issue_Boundary           11.000
edgecov.Coalition_Faces_Legislative_Threat              13.000
edgecov.Coalition_Focuses_on_Authorizing_Legislation    23.000
edgecov.Coalition_Steering_Committee                    20.000


Sample statistics cross-correlations:
                                                           edges
edges                                                  1.0000000
edgecov.Partisan_Diversity                             0.9595458
edgecov.Network_Embeddedness_Strong                    0.8960918
b1star2                                                0.8032081
edgecov.Visibility                                     0.9442850
edgecov.Controversial                                  0.4978858
edgecov.Interest_Group_Coalition_Partisan_Differential 0.7803491
edgecov.Number_of_Coalition_Memberships                0.9135127
edgecov.Coalition_Size                                 0.9135706
edgecov.Interest_Group_Partisanship                    0.2056758
edgecov.Coalition_Partisanship                         0.3105906
edgecov.Interest_Group_Age                             0.8695489
edgecov.Coalition_Age                                  0.7026143
edgecov.Citizens_Advocacy_Group                        0.4835913
edgecov.Coalition_Dues                                 0.6711365
edgecov.Lobbying_Expenditures                          0.3733120
edgecov.Interest_Group_Crosses_Issue_Boundary          0.3666408
edgecov.Coalition_Faces_Legislative_Threat             0.4842344
edgecov.Coalition_Focuses_on_Authorizing_Legislation   0.8091125
edgecov.Coalition_Steering_Committee                   0.7523139
                                                       edgecov.Partisan_Diversity
edges                                                                   0.9595458
edgecov.Partisan_Diversity                                              1.0000000
edgecov.Network_Embeddedness_Strong                                     0.8518328
b1star2                                                                 0.7676105
edgecov.Visibility                                                      0.8749526
edgecov.Controversial                                                   0.5367994
edgecov.Interest_Group_Coalition_Partisan_Differential                  0.8239526
edgecov.Number_of_Coalition_Memberships                                 0.8720612
edgecov.Coalition_Size                                                  0.8738472
edgecov.Interest_Group_Partisanship                                     0.1433614
edgecov.Coalition_Partisanship                                          0.2147006
edgecov.Interest_Group_Age                                              0.8198548
edgecov.Coalition_Age                                                   0.6365305
edgecov.Citizens_Advocacy_Group                                         0.4749754
edgecov.Coalition_Dues                                                  0.6191806
edgecov.Lobbying_Expenditures                                           0.3647788
edgecov.Interest_Group_Crosses_Issue_Boundary                           0.4238481
edgecov.Coalition_Faces_Legislative_Threat                              0.5207521
edgecov.Coalition_Focuses_on_Authorizing_Legislation                    0.7993368
edgecov.Coalition_Steering_Committee                                    0.7186578
                                                       edgecov.Network_Embeddedness_Strong
edges                                                                            0.8960918
edgecov.Partisan_Diversity                                                       0.8518328
edgecov.Network_Embeddedness_Strong                                              1.0000000
b1star2                                                                          0.8731920
edgecov.Visibility                                                               0.8387828
edgecov.Controversial                                                            0.4424143
edgecov.Interest_Group_Coalition_Partisan_Differential                           0.6746375
edgecov.Number_of_Coalition_Memberships                                          0.9584677
edgecov.Coalition_Size                                                           0.8047456
edgecov.Interest_Group_Partisanship                                              0.1137504
edgecov.Coalition_Partisanship                                                   0.2507915
edgecov.Interest_Group_Age                                                       0.8482345
edgecov.Coalition_Age                                                            0.5947647
edgecov.Citizens_Advocacy_Group                                                  0.4101170
edgecov.Coalition_Dues                                                           0.5852732
edgecov.Lobbying_Expenditures                                                    0.3323676
edgecov.Interest_Group_Crosses_Issue_Boundary                                    0.2873943
edgecov.Coalition_Faces_Legislative_Threat                                       0.4498801
edgecov.Coalition_Focuses_on_Authorizing_Legislation                             0.6937456
edgecov.Coalition_Steering_Committee                                             0.6451785
                                                         b1star2
edges                                                  0.8032081
edgecov.Partisan_Diversity                             0.7676105
edgecov.Network_Embeddedness_Strong                    0.8731920
b1star2                                                1.0000000
edgecov.Visibility                                     0.7633114
edgecov.Controversial                                  0.4244763
edgecov.Interest_Group_Coalition_Partisan_Differential 0.6297057
edgecov.Number_of_Coalition_Memberships                0.9080444
edgecov.Coalition_Size                                 0.7382041
edgecov.Interest_Group_Partisanship                    0.2217423
edgecov.Coalition_Partisanship                         0.3031216
edgecov.Interest_Group_Age                             0.8466317
edgecov.Coalition_Age                                  0.5222668
edgecov.Citizens_Advocacy_Group                        0.4004536
edgecov.Coalition_Dues                                 0.5329764
edgecov.Lobbying_Expenditures                          0.4419858
edgecov.Interest_Group_Crosses_Issue_Boundary          0.2480723
edgecov.Coalition_Faces_Legislative_Threat             0.3889745
edgecov.Coalition_Focuses_on_Authorizing_Legislation   0.6227032
edgecov.Coalition_Steering_Committee                   0.5953822
                                                       edgecov.Visibility
edges                                                           0.9442850
edgecov.Partisan_Diversity                                      0.8749526
edgecov.Network_Embeddedness_Strong                             0.8387828
b1star2                                                         0.7633114
edgecov.Visibility                                              1.0000000
edgecov.Controversial                                           0.4525896
edgecov.Interest_Group_Coalition_Partisan_Differential          0.7129262
edgecov.Number_of_Coalition_Memberships                         0.8630202
edgecov.Coalition_Size                                          0.8586254
edgecov.Interest_Group_Partisanship                             0.2460015
edgecov.Coalition_Partisanship                                  0.3893166
edgecov.Interest_Group_Age                                      0.8334653
edgecov.Coalition_Age                                           0.7109324
edgecov.Citizens_Advocacy_Group                                 0.4287590
edgecov.Coalition_Dues                                          0.7084227
edgecov.Lobbying_Expenditures                                   0.3738971
edgecov.Interest_Group_Crosses_Issue_Boundary                   0.3176051
edgecov.Coalition_Faces_Legislative_Threat                      0.2760447
edgecov.Coalition_Focuses_on_Authorizing_Legislation            0.6990073
edgecov.Coalition_Steering_Committee                            0.7390839
                                                       edgecov.Controversial
edges                                                              0.4978858
edgecov.Partisan_Diversity                                         0.5367994
edgecov.Network_Embeddedness_Strong                                0.4424143
b1star2                                                            0.4244763
edgecov.Visibility                                                 0.4525896
edgecov.Controversial                                              1.0000000
edgecov.Interest_Group_Coalition_Partisan_Differential             0.4488943
edgecov.Number_of_Coalition_Memberships                            0.4455031
edgecov.Coalition_Size                                             0.4080989
edgecov.Interest_Group_Partisanship                                0.1212363
edgecov.Coalition_Partisanship                                     0.1913235
edgecov.Interest_Group_Age                                         0.4133552
edgecov.Coalition_Age                                              0.2754251
edgecov.Citizens_Advocacy_Group                                    0.2463138
edgecov.Coalition_Dues                                             0.3467169
edgecov.Lobbying_Expenditures                                      0.2405702
edgecov.Interest_Group_Crosses_Issue_Boundary                      0.2990915
edgecov.Coalition_Faces_Legislative_Threat                         0.3786670
edgecov.Coalition_Focuses_on_Authorizing_Legislation               0.5187414
edgecov.Coalition_Steering_Committee                               0.4321740
                                                       edgecov.Interest_Group_Coalition_Partisan_Differential
edges                                                                                              0.78034907
edgecov.Partisan_Diversity                                                                         0.82395255
edgecov.Network_Embeddedness_Strong                                                                0.67463751
b1star2                                                                                            0.62970573
edgecov.Visibility                                                                                 0.71292621
edgecov.Controversial                                                                              0.44889429
edgecov.Interest_Group_Coalition_Partisan_Differential                                             1.00000000
edgecov.Number_of_Coalition_Memberships                                                            0.70917335
edgecov.Coalition_Size                                                                             0.70634159
edgecov.Interest_Group_Partisanship                                                                0.07236603
edgecov.Coalition_Partisanship                                                                     0.16228713
edgecov.Interest_Group_Age                                                                         0.67141766
edgecov.Coalition_Age                                                                              0.51455552
edgecov.Citizens_Advocacy_Group                                                                    0.35165164
edgecov.Coalition_Dues                                                                             0.51380905
edgecov.Lobbying_Expenditures                                                                      0.41851902
edgecov.Interest_Group_Crosses_Issue_Boundary                                                      0.39097890
edgecov.Coalition_Faces_Legislative_Threat                                                         0.41584664
edgecov.Coalition_Focuses_on_Authorizing_Legislation                                               0.64371691
edgecov.Coalition_Steering_Committee                                                               0.57854166
                                                       edgecov.Number_of_Coalition_Memberships
edges                                                                                0.9135127
edgecov.Partisan_Diversity                                                           0.8720612
edgecov.Network_Embeddedness_Strong                                                  0.9584677
b1star2                                                                              0.9080444
edgecov.Visibility                                                                   0.8630202
edgecov.Controversial                                                                0.4455031
edgecov.Interest_Group_Coalition_Partisan_Differential                               0.7091734
edgecov.Number_of_Coalition_Memberships                                              1.0000000
edgecov.Coalition_Size                                                               0.8492661
edgecov.Interest_Group_Partisanship                                                  0.1070185
edgecov.Coalition_Partisanship                                                       0.2300482
edgecov.Interest_Group_Age                                                           0.8979671
edgecov.Coalition_Age                                                                0.6319404
edgecov.Citizens_Advocacy_Group                                                      0.4213365
edgecov.Coalition_Dues                                                               0.5919600
edgecov.Lobbying_Expenditures                                                        0.3841782
edgecov.Interest_Group_Crosses_Issue_Boundary                                        0.2502911
edgecov.Coalition_Faces_Legislative_Threat                                           0.4273512
edgecov.Coalition_Focuses_on_Authorizing_Legislation                                 0.6957688
edgecov.Coalition_Steering_Committee                                                 0.6564190
                                                       edgecov.Coalition_Size
edges                                                               0.9135706
edgecov.Partisan_Diversity                                          0.8738472
edgecov.Network_Embeddedness_Strong                                 0.8047456
b1star2                                                             0.7382041
edgecov.Visibility                                                  0.8586254
edgecov.Controversial                                               0.4080989
edgecov.Interest_Group_Coalition_Partisan_Differential              0.7063416
edgecov.Number_of_Coalition_Memberships                             0.8492661
edgecov.Coalition_Size                                              1.0000000
edgecov.Interest_Group_Partisanship                                 0.1344948
edgecov.Coalition_Partisanship                                      0.1882228
edgecov.Interest_Group_Age                                          0.8073698
edgecov.Coalition_Age                                               0.6991993
edgecov.Citizens_Advocacy_Group                                     0.4608849
edgecov.Coalition_Dues                                              0.5813506
edgecov.Lobbying_Expenditures                                       0.3053275
edgecov.Interest_Group_Crosses_Issue_Boundary                       0.2766161
edgecov.Coalition_Faces_Legislative_Threat                          0.4249445
edgecov.Coalition_Focuses_on_Authorizing_Legislation                0.6708797
edgecov.Coalition_Steering_Committee                                0.7054294
                                                       edgecov.Interest_Group_Partisanship
edges                                                                           0.20567577
edgecov.Partisan_Diversity                                                      0.14336138
edgecov.Network_Embeddedness_Strong                                             0.11375041
b1star2                                                                         0.22174229
edgecov.Visibility                                                              0.24600150
edgecov.Controversial                                                           0.12123629
edgecov.Interest_Group_Coalition_Partisan_Differential                          0.07236603
edgecov.Number_of_Coalition_Memberships                                         0.10701853
edgecov.Coalition_Size                                                          0.13449480
edgecov.Interest_Group_Partisanship                                             1.00000000
edgecov.Coalition_Partisanship                                                  0.65132455
edgecov.Interest_Group_Age                                                      0.16943393
edgecov.Coalition_Age                                                           0.07210304
edgecov.Citizens_Advocacy_Group                                                -0.05686171
edgecov.Coalition_Dues                                                          0.20728768
edgecov.Lobbying_Expenditures                                                   0.43824556
edgecov.Interest_Group_Crosses_Issue_Boundary                                   0.09402232
edgecov.Coalition_Faces_Legislative_Threat                                      0.05647914
edgecov.Coalition_Focuses_on_Authorizing_Legislation                            0.22675835
edgecov.Coalition_Steering_Committee                                            0.21719573
                                                       edgecov.Coalition_Partisanship
edges                                                                      0.31059063
edgecov.Partisan_Diversity                                                 0.21470057
edgecov.Network_Embeddedness_Strong                                        0.25079155
b1star2                                                                    0.30312156
edgecov.Visibility                                                         0.38931659
edgecov.Controversial                                                      0.19132350
edgecov.Interest_Group_Coalition_Partisan_Differential                     0.16228713
edgecov.Number_of_Coalition_Memberships                                    0.23004821
edgecov.Coalition_Size                                                     0.18822279
edgecov.Interest_Group_Partisanship                                        0.65132455
edgecov.Coalition_Partisanship                                             1.00000000
edgecov.Interest_Group_Age                                                 0.26113654
edgecov.Coalition_Age                                                      0.11959615
edgecov.Citizens_Advocacy_Group                                           -0.08115175
edgecov.Coalition_Dues                                                     0.31147236
edgecov.Lobbying_Expenditures                                              0.37487865
edgecov.Interest_Group_Crosses_Issue_Boundary                              0.16083438
edgecov.Coalition_Faces_Legislative_Threat                                 0.06653795
edgecov.Coalition_Focuses_on_Authorizing_Legislation                       0.32374978
edgecov.Coalition_Steering_Committee                                       0.32924367
                                                       edgecov.Interest_Group_Age
edges                                                                   0.8695489
edgecov.Partisan_Diversity                                              0.8198548
edgecov.Network_Embeddedness_Strong                                     0.8482345
b1star2                                                                 0.8466317
edgecov.Visibility                                                      0.8334653
edgecov.Controversial                                                   0.4133552
edgecov.Interest_Group_Coalition_Partisan_Differential                  0.6714177
edgecov.Number_of_Coalition_Memberships                                 0.8979671
edgecov.Coalition_Size                                                  0.8073698
edgecov.Interest_Group_Partisanship                                     0.1694339
edgecov.Coalition_Partisanship                                          0.2611365
edgecov.Interest_Group_Age                                              1.0000000
edgecov.Coalition_Age                                                   0.6065233
edgecov.Citizens_Advocacy_Group                                         0.3578140
edgecov.Coalition_Dues                                                  0.5823287
edgecov.Lobbying_Expenditures                                           0.3150472
edgecov.Interest_Group_Crosses_Issue_Boundary                           0.2731538
edgecov.Coalition_Faces_Legislative_Threat                              0.3871101
edgecov.Coalition_Focuses_on_Authorizing_Legislation                    0.6568435
edgecov.Coalition_Steering_Committee                                    0.6473482
                                                       edgecov.Coalition_Age
edges                                                             0.70261426
edgecov.Partisan_Diversity                                        0.63653051
edgecov.Network_Embeddedness_Strong                               0.59476474
b1star2                                                           0.52226682
edgecov.Visibility                                                0.71093242
edgecov.Controversial                                             0.27542509
edgecov.Interest_Group_Coalition_Partisan_Differential            0.51455552
edgecov.Number_of_Coalition_Memberships                           0.63194045
edgecov.Coalition_Size                                            0.69919925
edgecov.Interest_Group_Partisanship                               0.07210304
edgecov.Coalition_Partisanship                                    0.11959615
edgecov.Interest_Group_Age                                        0.60652329
edgecov.Coalition_Age                                             1.00000000
edgecov.Citizens_Advocacy_Group                                   0.43644872
edgecov.Coalition_Dues                                            0.66860646
edgecov.Lobbying_Expenditures                                     0.17899552
edgecov.Interest_Group_Crosses_Issue_Boundary                     0.18787471
edgecov.Coalition_Faces_Legislative_Threat                        0.18206332
edgecov.Coalition_Focuses_on_Authorizing_Legislation              0.54107329
edgecov.Coalition_Steering_Committee                              0.52354240
                                                       edgecov.Citizens_Advocacy_Group
edges                                                                       0.48359135
edgecov.Partisan_Diversity                                                  0.47497543
edgecov.Network_Embeddedness_Strong                                         0.41011701
b1star2                                                                     0.40045362
edgecov.Visibility                                                          0.42875899
edgecov.Controversial                                                       0.24631383
edgecov.Interest_Group_Coalition_Partisan_Differential                      0.35165164
edgecov.Number_of_Coalition_Memberships                                     0.42133650
edgecov.Coalition_Size                                                      0.46088491
edgecov.Interest_Group_Partisanship                                        -0.05686171
edgecov.Coalition_Partisanship                                             -0.08115175
edgecov.Interest_Group_Age                                                  0.35781404
edgecov.Coalition_Age                                                       0.43644872
edgecov.Citizens_Advocacy_Group                                             1.00000000
edgecov.Coalition_Dues                                                      0.31977807
edgecov.Lobbying_Expenditures                                               0.05170633
edgecov.Interest_Group_Crosses_Issue_Boundary                               0.19363867
edgecov.Coalition_Faces_Legislative_Threat                                  0.27596399
edgecov.Coalition_Focuses_on_Authorizing_Legislation                        0.39467284
edgecov.Coalition_Steering_Committee                                        0.33317165
                                                       edgecov.Coalition_Dues
edges                                                               0.6711365
edgecov.Partisan_Diversity                                          0.6191806
edgecov.Network_Embeddedness_Strong                                 0.5852732
b1star2                                                             0.5329764
edgecov.Visibility                                                  0.7084227
edgecov.Controversial                                               0.3467169
edgecov.Interest_Group_Coalition_Partisan_Differential              0.5138090
edgecov.Number_of_Coalition_Memberships                             0.5919600
edgecov.Coalition_Size                                              0.5813506
edgecov.Interest_Group_Partisanship                                 0.2072877
edgecov.Coalition_Partisanship                                      0.3114724
edgecov.Interest_Group_Age                                          0.5823287
edgecov.Coalition_Age                                               0.6686065
edgecov.Citizens_Advocacy_Group                                     0.3197781
edgecov.Coalition_Dues                                              1.0000000
edgecov.Lobbying_Expenditures                                       0.2847189
edgecov.Interest_Group_Crosses_Issue_Boundary                       0.2416420
edgecov.Coalition_Faces_Legislative_Threat                          0.1649837
edgecov.Coalition_Focuses_on_Authorizing_Legislation                0.5753124
edgecov.Coalition_Steering_Committee                                0.5540053
                                                       edgecov.Lobbying_Expenditures
edges                                                                     0.37331195
edgecov.Partisan_Diversity                                                0.36477875
edgecov.Network_Embeddedness_Strong                                       0.33236757
b1star2                                                                   0.44198578
edgecov.Visibility                                                        0.37389714
edgecov.Controversial                                                     0.24057024
edgecov.Interest_Group_Coalition_Partisan_Differential                    0.41851902
edgecov.Number_of_Coalition_Memberships                                   0.38417824
edgecov.Coalition_Size                                                    0.30532749
edgecov.Interest_Group_Partisanship                                       0.43824556
edgecov.Coalition_Partisanship                                            0.37487865
edgecov.Interest_Group_Age                                                0.31504724
edgecov.Coalition_Age                                                     0.17899552
edgecov.Citizens_Advocacy_Group                                           0.05170633
edgecov.Coalition_Dues                                                    0.28471893
edgecov.Lobbying_Expenditures                                             1.00000000
edgecov.Interest_Group_Crosses_Issue_Boundary                             0.14818734
edgecov.Coalition_Faces_Legislative_Threat                                0.18998295
edgecov.Coalition_Focuses_on_Authorizing_Legislation                      0.34210980
edgecov.Coalition_Steering_Committee                                      0.31528316
                                                       edgecov.Interest_Group_Crosses_Issue_Boundary
edges                                                                                     0.36664075
edgecov.Partisan_Diversity                                                                0.42384813
edgecov.Network_Embeddedness_Strong                                                       0.28739427
b1star2                                                                                   0.24807229
edgecov.Visibility                                                                        0.31760512
edgecov.Controversial                                                                     0.29909151
edgecov.Interest_Group_Coalition_Partisan_Differential                                    0.39097890
edgecov.Number_of_Coalition_Memberships                                                   0.25029105
edgecov.Coalition_Size                                                                    0.27661608
edgecov.Interest_Group_Partisanship                                                       0.09402232
edgecov.Coalition_Partisanship                                                            0.16083438
edgecov.Interest_Group_Age                                                                0.27315377
edgecov.Coalition_Age                                                                     0.18787471
edgecov.Citizens_Advocacy_Group                                                           0.19363867
edgecov.Coalition_Dues                                                                    0.24164201
edgecov.Lobbying_Expenditures                                                             0.14818734
edgecov.Interest_Group_Crosses_Issue_Boundary                                             1.00000000
edgecov.Coalition_Faces_Legislative_Threat                                                0.26866331
edgecov.Coalition_Focuses_on_Authorizing_Legislation                                      0.41786991
edgecov.Coalition_Steering_Committee                                                      0.34392694
                                                       edgecov.Coalition_Faces_Legislative_Threat
edges                                                                                  0.48423438
edgecov.Partisan_Diversity                                                             0.52075213
edgecov.Network_Embeddedness_Strong                                                    0.44988010
b1star2                                                                                0.38897453
edgecov.Visibility                                                                     0.27604470
edgecov.Controversial                                                                  0.37866700
edgecov.Interest_Group_Coalition_Partisan_Differential                                 0.41584664
edgecov.Number_of_Coalition_Memberships                                                0.42735121
edgecov.Coalition_Size                                                                 0.42494447
edgecov.Interest_Group_Partisanship                                                    0.05647914
edgecov.Coalition_Partisanship                                                         0.06653795
edgecov.Interest_Group_Age                                                             0.38711011
edgecov.Coalition_Age                                                                  0.18206332
edgecov.Citizens_Advocacy_Group                                                        0.27596399
edgecov.Coalition_Dues                                                                 0.16498365
edgecov.Lobbying_Expenditures                                                          0.18998295
edgecov.Interest_Group_Crosses_Issue_Boundary                                          0.26866331
edgecov.Coalition_Faces_Legislative_Threat                                             1.00000000
edgecov.Coalition_Focuses_on_Authorizing_Legislation                                   0.60336272
edgecov.Coalition_Steering_Committee                                                   0.36411814
                                                       edgecov.Coalition_Focuses_on_Authorizing_Legislation
edges                                                                                             0.8091125
edgecov.Partisan_Diversity                                                                        0.7993368
edgecov.Network_Embeddedness_Strong                                                               0.6937456
b1star2                                                                                           0.6227032
edgecov.Visibility                                                                                0.6990073
edgecov.Controversial                                                                             0.5187414
edgecov.Interest_Group_Coalition_Partisan_Differential                                            0.6437169
edgecov.Number_of_Coalition_Memberships                                                           0.6957688
edgecov.Coalition_Size                                                                            0.6708797
edgecov.Interest_Group_Partisanship                                                               0.2267584
edgecov.Coalition_Partisanship                                                                    0.3237498
edgecov.Interest_Group_Age                                                                        0.6568435
edgecov.Coalition_Age                                                                             0.5410733
edgecov.Citizens_Advocacy_Group                                                                   0.3946728
edgecov.Coalition_Dues                                                                            0.5753124
edgecov.Lobbying_Expenditures                                                                     0.3421098
edgecov.Interest_Group_Crosses_Issue_Boundary                                                     0.4178699
edgecov.Coalition_Faces_Legislative_Threat                                                        0.6033627
edgecov.Coalition_Focuses_on_Authorizing_Legislation                                              1.0000000
edgecov.Coalition_Steering_Committee                                                              0.6684224
                                                       edgecov.Coalition_Steering_Committee
edges                                                                             0.7523139
edgecov.Partisan_Diversity                                                        0.7186578
edgecov.Network_Embeddedness_Strong                                               0.6451785
b1star2                                                                           0.5953822
edgecov.Visibility                                                                0.7390839
edgecov.Controversial                                                             0.4321740
edgecov.Interest_Group_Coalition_Partisan_Differential                            0.5785417
edgecov.Number_of_Coalition_Memberships                                           0.6564190
edgecov.Coalition_Size                                                            0.7054294
edgecov.Interest_Group_Partisanship                                               0.2171957
edgecov.Coalition_Partisanship                                                    0.3292437
edgecov.Interest_Group_Age                                                        0.6473482
edgecov.Coalition_Age                                                             0.5235424
edgecov.Citizens_Advocacy_Group                                                   0.3331716
edgecov.Coalition_Dues                                                            0.5540053
edgecov.Lobbying_Expenditures                                                     0.3152832
edgecov.Interest_Group_Crosses_Issue_Boundary                                     0.3439269
edgecov.Coalition_Faces_Legislative_Threat                                        0.3641181
edgecov.Coalition_Focuses_on_Authorizing_Legislation                              0.6684224
edgecov.Coalition_Steering_Committee                                              1.0000000

Sample statistics auto-correlation:
Chain 1 
             edges edgecov.Partisan_Diversity
Lag 0    1.0000000                  1.0000000
Lag 1024 0.8817577                  0.8833440
Lag 2048 0.7826895                  0.7860786
Lag 3072 0.6973019                  0.7023755
Lag 4096 0.6224734                  0.6300390
Lag 5120 0.5569923                  0.5669054
         edgecov.Network_Embeddedness_Strong   b1star2 edgecov.Visibility
Lag 0                              1.0000000 1.0000000          1.0000000
Lag 1024                           0.9077394 0.9282928          0.8832079
Lag 2048                           0.8268250 0.8642873          0.7856256
Lag 3072                           0.7548016 0.8060468          0.7016340
Lag 4096                           0.6896322 0.7519813          0.6269764
Lag 5120                           0.6304936 0.7023110          0.5617121
         edgecov.Controversial
Lag 0                1.0000000
Lag 1024             0.8384684
Lag 2048             0.7124686
Lag 3072             0.6131609
Lag 4096             0.5340141
Lag 5120             0.4672294
         edgecov.Interest_Group_Coalition_Partisan_Differential
Lag 0                                                 1.0000000
Lag 1024                                              0.8688828
Lag 2048                                              0.7620797
Lag 3072                                              0.6716029
Lag 4096                                              0.5951611
Lag 5120                                              0.5289013
         edgecov.Number_of_Coalition_Memberships edgecov.Coalition_Size
Lag 0                                  1.0000000              1.0000000
Lag 1024                               0.9038003              0.8451683
Lag 2048                               0.8208401              0.7220680
Lag 3072                               0.7482327              0.6226504
Lag 4096                               0.6825427              0.5406140
Lag 5120                               0.6231631              0.4716208
         edgecov.Interest_Group_Partisanship edgecov.Coalition_Partisanship
Lag 0                              1.0000000                      1.0000000
Lag 1024                           0.8892855                      0.8949835
Lag 2048                           0.7974525                      0.8048172
Lag 3072                           0.7169330                      0.7247629
Lag 4096                           0.6487843                      0.6557026
Lag 5120                           0.5901335                      0.5956513
         edgecov.Interest_Group_Age edgecov.Coalition_Age
Lag 0                     1.0000000             1.0000000
Lag 1024                  0.9061529             0.8675561
Lag 2048                  0.8253035             0.7557814
Lag 3072                  0.7535421             0.6599947
Lag 4096                  0.6892392             0.5776616
Lag 5120                  0.6316183             0.5046197
         edgecov.Citizens_Advocacy_Group edgecov.Coalition_Dues
Lag 0                          1.0000000              1.0000000
Lag 1024                       0.8890000              0.8691591
Lag 2048                       0.7938163              0.7599663
Lag 3072                       0.7104077              0.6683767
Lag 4096                       0.6401958              0.5906078
Lag 5120                       0.5789503              0.5243038
         edgecov.Lobbying_Expenditures
Lag 0                        1.0000000
Lag 1024                     0.9279495
Lag 2048                     0.8631763
Lag 3072                     0.8039020
Lag 4096                     0.7502466
Lag 5120                     0.7017687
         edgecov.Interest_Group_Crosses_Issue_Boundary
Lag 0                                        1.0000000
Lag 1024                                     0.8693908
Lag 2048                                     0.7641691
Lag 3072                                     0.6775283
Lag 4096                                     0.6036618
Lag 5120                                     0.5406167
         edgecov.Coalition_Faces_Legislative_Threat
Lag 0                                     1.0000000
Lag 1024                                  0.8396991
Lag 2048                                  0.7104171
Lag 3072                                  0.6049028
Lag 4096                                  0.5187892
Lag 5120                                  0.4500025
         edgecov.Coalition_Focuses_on_Authorizing_Legislation
Lag 0                                               1.0000000
Lag 1024                                            0.8725429
Lag 2048                                            0.7669923
Lag 3072                                            0.6760151
Lag 4096                                            0.5992657
Lag 5120                                            0.5336343
         edgecov.Coalition_Steering_Committee
Lag 0                               1.0000000
Lag 1024                            0.8690016
Lag 2048                            0.7624690
Lag 3072                            0.6726527
Lag 4096                            0.5943587
Lag 5120                            0.5283499

Sample statistics burn-in diagnostic (Geweke):
Chain 1 

Fraction in 1st window = 0.1
Fraction in 2nd window = 0.5 

                                                 edges 
                                                1.6771 
                            edgecov.Partisan_Diversity 
                                                1.8205 
                   edgecov.Network_Embeddedness_Strong 
                                                0.9457 
                                               b1star2 
                                                1.0797 
                                    edgecov.Visibility 
                                                1.7619 
                                 edgecov.Controversial 
                                                1.4372 
edgecov.Interest_Group_Coalition_Partisan_Differential 
                                                2.8059 
               edgecov.Number_of_Coalition_Memberships 
                                                1.0796 
                                edgecov.Coalition_Size 
                                                1.4063 
                   edgecov.Interest_Group_Partisanship 
                                                0.8664 
                        edgecov.Coalition_Partisanship 
                                                0.5522 
                            edgecov.Interest_Group_Age 
                                                1.7954 
                                 edgecov.Coalition_Age 
                                                1.7328 
                       edgecov.Citizens_Advocacy_Group 
                                                0.4327 
                                edgecov.Coalition_Dues 
                                                2.0625 
                         edgecov.Lobbying_Expenditures 
                                                0.2719 
         edgecov.Interest_Group_Crosses_Issue_Boundary 
                                                1.2711 
            edgecov.Coalition_Faces_Legislative_Threat 
                                                0.5704 
  edgecov.Coalition_Focuses_on_Authorizing_Legislation 
                                                2.0863 
                  edgecov.Coalition_Steering_Committee 
                                                0.9351 

Individual P-values (lower = worse):
                                                 edges 
                                           0.093529569 
                            edgecov.Partisan_Diversity 
                                           0.068677926 
                   edgecov.Network_Embeddedness_Strong 
                                           0.344318970 
                                               b1star2 
                                           0.280294990 
                                    edgecov.Visibility 
                                           0.078084435 
                                 edgecov.Controversial 
                                           0.150648530 
edgecov.Interest_Group_Coalition_Partisan_Differential 
                                           0.005018223 
               edgecov.Number_of_Coalition_Memberships 
                                           0.280305412 
                                edgecov.Coalition_Size 
                                           0.159637092 
                   edgecov.Interest_Group_Partisanship 
                                           0.386260668 
                        edgecov.Coalition_Partisanship 
                                           0.580826034 
                            edgecov.Interest_Group_Age 
                                           0.072582739 
                                 edgecov.Coalition_Age 
                                           0.083127384 
                       edgecov.Citizens_Advocacy_Group 
                                           0.665218157 
                                edgecov.Coalition_Dues 
                                           0.039156701 
                         edgecov.Lobbying_Expenditures 
                                           0.785716920 
         edgecov.Interest_Group_Crosses_Issue_Boundary 
                                           0.203685694 
            edgecov.Coalition_Faces_Legislative_Threat 
                                           0.568413369 
  edgecov.Coalition_Focuses_on_Authorizing_Legislation 
                                           0.036953657 
                  edgecov.Coalition_Steering_Committee 
                                           0.349759435 
Joint P-value (lower = worse):  0.3100703 .
Loading required namespace: latticeExtra

MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).
> dev.off()
null device 
          1 
> 
> 
> # ==============================================================================
> # Estimate a random or fixed effects model
> # ==============================================================================
> 
> rows <- nrow(as.matrix(leader))
> cols <- ncol(as.matrix(leader))
> ig <- matrix(rep(1:rows, cols), nrow = rows)
> coal <- matrix(rep(1:cols, rows), ncol = cols, byrow = TRUE)
> 
> # create data frame
> nm <- as.matrix(nonmem)
> dat <- data.frame(
+     leader = as.matrix(leader)[nm != 1], 
+     absdiff = Interest_Group_Coalition_Partisan_Differential[nm != 1], 
+     Partisan_Diversity = Partisan_Diversity[nm != 1], 
+     Network_Embeddedness_Strong = Network_Embeddedness_Strong[nm != 1], 
+     Interest_Group_Partisanship = Interest_Group_Partisanship[nm != 1], 
+     Coalition_Partisanship = Coalition_Partisanship[nm != 1], 
+     Interest_Group_Age = Interest_Group_Age[nm != 1], 
+     Coalition_Age = Coalition_Age[nm != 1], 
+     Citizens_Advocacy_Group = Citizens_Advocacy_Group[nm != 1], 
+     Coalition_Dues = Coalition_Dues[nm != 1], 
+     Lobbying_Expenditures = Lobbying_Expenditures[nm != 1], 
+     Interest_Group_Crosses_Issue_Boundary = 
+         Interest_Group_Crosses_Issue_Boundary[nm != 1], 
+     Coalition_Faces_Legislative_Threat = Coalition_Faces_Legislative_Threat[nm 
+         != 1], 
+     Coalition_Focuses_on_Authorizing_Legislation = 
+         Coalition_Focuses_on_Authorizing_Legislation[nm != 1], 
+     Coalition_Steering_Committee = Coalition_Steering_Committee[nm != 1], 
+     ig = ig[nm != 1], 
+     coal = coal[nm != 1]
+ )
> dat$coal2 <- factor(dat$coal)  # fixed effect: create factor
> 
> # random effect in lme4: estimation does not converge
> library("lme4")
Loading required package: Matrix

Attaching package: ‘lme4’

The following object is masked from ‘package:stats’:

    sigma

> model.19 <- glmer(
+     leader
+     ~ absdiff 
+     + Partisan_Diversity
+     + Network_Embeddedness_Strong
+     + Interest_Group_Partisanship 
+     + Coalition_Partisanship 
+     + Interest_Group_Age 
+     + Coalition_Age 
+     + Citizens_Advocacy_Group 
+     + Coalition_Dues 
+     + Lobbying_Expenditures 
+     + Interest_Group_Crosses_Issue_Boundary 
+     + Coalition_Faces_Legislative_Threat 
+     + Coalition_Focuses_on_Authorizing_Legislation 
+     + Coalition_Steering_Committee 
+     + (1 | coal), 
+     data = dat[, 1:17], family = binomial, nAGQ = 10
+ )
Warning message:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model failed to converge with max|grad| = 0.0207821 (tol = 0.001, component 1)
> summary(model.19)
Generalized linear mixed model fit by maximum likelihood (Adaptive
  Gauss-Hermite Quadrature, nAGQ = 10) [glmerMod]
 Family: binomial  ( logit )
Formula: leader ~ absdiff + Partisan_Diversity + Network_Embeddedness_Strong +  
    Interest_Group_Partisanship + Coalition_Partisanship + Interest_Group_Age +  
    Coalition_Age + Citizens_Advocacy_Group + Coalition_Dues +  
    Lobbying_Expenditures + Interest_Group_Crosses_Issue_Boundary +  
    Coalition_Faces_Legislative_Threat + Coalition_Focuses_on_Authorizing_Legislation +  
    Coalition_Steering_Committee + (1 | coal)
   Data: dat[, 1:17]

     AIC      BIC   logLik deviance df.resid 
  1150.4   1230.0   -559.2   1118.4     1054 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.5988 -0.5541 -0.4216  0.5267  3.4675 

Random effects:
 Groups Name        Variance Std.Dev.
 coal   (Intercept) 0.4901   0.7001  
Number of obs: 1070, groups:  coal, 74

Fixed effects:
                                              Estimate Std. Error z value
(Intercept)                                  -3.409552   0.516225  -6.605
absdiff                                      -0.005329   0.030118  -0.177
Partisan_Diversity                            0.229805   0.087352   2.631
Network_Embeddedness_Strong                  17.719250   4.462086   3.971
Interest_Group_Partisanship                   0.037971   0.018650   2.036
Coalition_Partisanship                        0.091592   0.040730   2.249
Interest_Group_Age                            0.345763   0.211140   1.638
Coalition_Age                                 0.621957   1.037526   0.599
Citizens_Advocacy_Group                       0.385606   0.191853   2.010
Coalition_Dues                               -0.005188   0.265177  -0.020
Lobbying_Expenditures                         0.006838   0.012386   0.552
Interest_Group_Crosses_Issue_Boundary        -0.027859   0.235564  -0.118
Coalition_Faces_Legislative_Threat           -0.308831   0.328188  -0.941
Coalition_Focuses_on_Authorizing_Legislation  0.202866   0.277408   0.731
Coalition_Steering_Committee                  0.225483   0.238032   0.947
                                             Pr(>|z|)    
(Intercept)                                  3.98e-11 ***
absdiff                                       0.85956    
Partisan_Diversity                            0.00852 ** 
Network_Embeddedness_Strong                  7.16e-05 ***
Interest_Group_Partisanship                   0.04175 *  
Coalition_Partisanship                        0.02453 *  
Interest_Group_Age                            0.10150    
Coalition_Age                                 0.54887    
Citizens_Advocacy_Group                       0.04444 *  
Coalition_Dues                                0.98439    
Lobbying_Expenditures                         0.58088    
Interest_Group_Crosses_Issue_Boundary         0.90586    
Coalition_Faces_Legislative_Threat            0.34669    
Coalition_Focuses_on_Authorizing_Legislation  0.46460    
Coalition_Steering_Committee                  0.34350    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) absdff Prts_D Nt_E_S In_G_P Cltn_P In_G_A Cltn_A Ct_A_G
absdiff     -0.071                                                        
Prtsn_Dvrst -0.728 -0.204                                                 
Ntwrk_Emb_S -0.372  0.136  0.056                                          
Intrst_Gr_P -0.083  0.172  0.001  0.192                                   
Cltn_Prtsns -0.231  0.003  0.250 -0.011 -0.357                            
Intrst_Gr_A -0.232 -0.058  0.037 -0.247 -0.062  0.017                     
Coalitin_Ag -0.329  0.007  0.153  0.093  0.017  0.220 -0.029              
Ctzns_Adv_G -0.183  0.054  0.029  0.050  0.037  0.135  0.146 -0.031       
Coalitin_Ds -0.061 -0.004  0.007 -0.045 -0.006 -0.107 -0.002 -0.320 -0.006
Lbbyng_Expn  0.052 -0.285 -0.030 -0.131 -0.346 -0.042  0.082  0.010  0.024
Int_G_C_I_B  0.090 -0.113 -0.164  0.044  0.021 -0.097 -0.005  0.031 -0.039
Cltn_Fc_L_T -0.001 -0.001 -0.075 -0.067 -0.018  0.120 -0.001  0.142 -0.007
Cltn_F__A_L -0.165  0.019 -0.084  0.046  0.001 -0.147  0.046 -0.035 -0.017
Cltn_Strn_C -0.202  0.012  0.027  0.053  0.018 -0.085 -0.011  0.011  0.029
            Cltn_D Lbby_E I_G_C_ C_F_L_ C_F__A
absdiff                                       
Prtsn_Dvrst                                   
Ntwrk_Emb_S                                   
Intrst_Gr_P                                   
Cltn_Prtsns                                   
Intrst_Gr_A                                   
Coalitin_Ag                                   
Ctzns_Adv_G                                   
Coalitin_Ds                                   
Lbbyng_Expn -0.021                            
Int_G_C_I_B  0.021  0.057                     
Cltn_Fc_L_T  0.203 -0.011  0.021              
Cltn_F__A_L -0.161 -0.009 -0.099 -0.430       
Cltn_Strn_C -0.069 -0.006 -0.045  0.028 -0.120
convergence code: 0
Model failed to converge with max|grad| = 0.0207821 (tol = 0.001, component 1)

> 
> # random effect with glmmPQL: estimation converges, but don't trust the results;
> # e.g., no model fit is reported... did it really converge?
> library("MASS")
> model.20 <- glmmPQL(
+     leader
+     ~ absdiff 
+     + Partisan_Diversity
+     + Network_Embeddedness_Strong
+     + Interest_Group_Partisanship 
+     + Coalition_Partisanship 
+     + Interest_Group_Age 
+     + Coalition_Age 
+     + Citizens_Advocacy_Group 
+     + Coalition_Dues 
+     + Lobbying_Expenditures 
+     + Interest_Group_Crosses_Issue_Boundary 
+     + Coalition_Faces_Legislative_Threat 
+     + Coalition_Focuses_on_Authorizing_Legislation 
+     + Coalition_Steering_Committee 
+     , random = ~ 1|coal
+     , data = dat[, 1:17], family = binomial
+ )
iteration 1
iteration 2
iteration 3
iteration 4
> summary(model.20)
Linear mixed-effects model fit by maximum likelihood
 Data: dat[, 1:17] 
  AIC BIC logLik
   NA  NA     NA

Random effects:
 Formula: ~1 | coal
        (Intercept)  Residual
StdDev:   0.6861973 0.9565505

Variance function:
 Structure: fixed weights
 Formula: ~invwt 
Fixed effects: leader ~ absdiff + Partisan_Diversity + Network_Embeddedness_Strong +      Interest_Group_Partisanship + Coalition_Partisanship + Interest_Group_Age +      Coalition_Age + Citizens_Advocacy_Group + Coalition_Dues +      Lobbying_Expenditures + Interest_Group_Crosses_Issue_Boundary +      Coalition_Faces_Legislative_Threat + Coalition_Focuses_on_Authorizing_Legislation +      Coalition_Steering_Committee 
                                                 Value Std.Error  DF   t-value
(Intercept)                                  -3.265299  0.493215 989 -6.620435
absdiff                                      -0.005113  0.028526 989 -0.179252
Partisan_Diversity                            0.217520  0.083938  66  2.591434
Network_Embeddedness_Strong                  17.219847  4.179088 989  4.120480
Interest_Group_Partisanship                   0.036751  0.017648 989  2.082405
Coalition_Partisanship                        0.086416  0.039052  66  2.212840
Interest_Group_Age                            0.332716  0.200044 989  1.663219
Coalition_Age                                 0.587014  0.999402  66  0.587365
Citizens_Advocacy_Group                       0.371213  0.181969 989  2.039978
Coalition_Dues                               -0.005462  0.255587  66 -0.021370
Lobbying_Expenditures                         0.006639  0.011704 989  0.567212
Interest_Group_Crosses_Issue_Boundary        -0.029656  0.223072 989 -0.132942
Coalition_Faces_Legislative_Threat           -0.297830  0.315865  66 -0.942902
Coalition_Focuses_on_Authorizing_Legislation  0.203688  0.267830  66  0.760514
Coalition_Steering_Committee                  0.214081  0.229017  66  0.934783
                                             p-value
(Intercept)                                   0.0000
absdiff                                       0.8578
Partisan_Diversity                            0.0118
Network_Embeddedness_Strong                   0.0000
Interest_Group_Partisanship                   0.0376
Coalition_Partisanship                        0.0304
Interest_Group_Age                            0.0966
Coalition_Age                                 0.5590
Citizens_Advocacy_Group                       0.0416
Coalition_Dues                                0.9830
Lobbying_Expenditures                         0.5707
Interest_Group_Crosses_Issue_Boundary         0.8943
Coalition_Faces_Legislative_Threat            0.3492
Coalition_Focuses_on_Authorizing_Legislation  0.4497
Coalition_Steering_Committee                  0.3533
 Correlation: 
                                             (Intr) absdff Prts_D Nt_E_S In_G_P
absdiff                                      -0.072                            
Partisan_Diversity                           -0.733 -0.201                     
Network_Embeddedness_Strong                  -0.361  0.140  0.058              
Interest_Group_Partisanship                  -0.079  0.171  0.003  0.186       
Coalition_Partisanship                       -0.232  0.003  0.248 -0.008 -0.352
Interest_Group_Age                           -0.230 -0.058  0.036 -0.250 -0.061
Coalition_Age                                -0.323  0.008  0.153  0.079  0.013
Citizens_Advocacy_Group                      -0.183  0.054  0.028  0.053  0.038
Coalition_Dues                               -0.067 -0.005  0.008 -0.039 -0.005
Lobbying_Expenditures                         0.055 -0.283 -0.030 -0.139 -0.349
Interest_Group_Crosses_Issue_Boundary         0.087 -0.114 -0.162  0.050  0.022
Coalition_Faces_Legislative_Threat           -0.005 -0.002 -0.072 -0.066 -0.018
Coalition_Focuses_on_Authorizing_Legislation -0.166  0.019 -0.086  0.045  0.001
Coalition_Steering_Committee                 -0.196  0.013  0.028  0.039  0.015
                                             Cltn_P In_G_A Cltn_A Ct_A_G Cltn_D
absdiff                                                                        
Partisan_Diversity                                                             
Network_Embeddedness_Strong                                                    
Interest_Group_Partisanship                                                    
Coalition_Partisanship                                                         
Interest_Group_Age                            0.017                            
Coalition_Age                                 0.224 -0.029                     
Citizens_Advocacy_Group                       0.134  0.146 -0.029              
Coalition_Dues                               -0.107 -0.002 -0.316 -0.007       
Lobbying_Expenditures                        -0.040  0.081  0.006  0.025 -0.019
Interest_Group_Crosses_Issue_Boundary        -0.096 -0.006  0.034 -0.039  0.020
Coalition_Faces_Legislative_Threat            0.119 -0.001  0.144 -0.007  0.203
Coalition_Focuses_on_Authorizing_Legislation -0.146  0.045 -0.035 -0.016 -0.162
Coalition_Steering_Committee                 -0.085 -0.010  0.003  0.030 -0.067
                                             Lbby_E I_G_C_ C_F_L_ C_F__A
absdiff                                                                 
Partisan_Diversity                                                      
Network_Embeddedness_Strong                                             
Interest_Group_Partisanship                                             
Coalition_Partisanship                                                  
Interest_Group_Age                                                      
Coalition_Age                                                           
Citizens_Advocacy_Group                                                 
Coalition_Dues                                                          
Lobbying_Expenditures                                                   
Interest_Group_Crosses_Issue_Boundary         0.059                     
Coalition_Faces_Legislative_Threat           -0.010  0.021              
Coalition_Focuses_on_Authorizing_Legislation -0.009 -0.098 -0.429       
Coalition_Steering_Committee                 -0.009 -0.041  0.026 -0.119

Standardized Within-Group Residuals:
       Min         Q1        Med         Q3        Max 
-1.6601724 -0.5849611 -0.4455570  0.5565609  3.5360176 

Number of Observations: 1070
Number of Groups: 74 
> 
> # use GLM and fixed effect: model does not converge
> model.21 <- glm(
+     leader
+     ~ absdiff 
+     + Partisan_Diversity
+     + Network_Embeddedness_Strong
+     + Interest_Group_Partisanship 
+     + Coalition_Partisanship 
+     + Interest_Group_Age 
+     + Coalition_Age 
+     + Citizens_Advocacy_Group 
+     + Coalition_Dues 
+     + Lobbying_Expenditures 
+     + Interest_Group_Crosses_Issue_Boundary 
+     + Coalition_Faces_Legislative_Threat 
+     + Coalition_Focuses_on_Authorizing_Legislation 
+     + Coalition_Steering_Committee
+     + coal2
+     , data = dat, family = binomial
+ )
Warning messages:
1: glm.fit: algorithm did not converge 
2: glm.fit: fitted probabilities numerically 0 or 1 occurred 
> summary(model.21)

Call:
glm(formula = leader ~ absdiff + Partisan_Diversity + Network_Embeddedness_Strong + 
    Interest_Group_Partisanship + Coalition_Partisanship + Interest_Group_Age + 
    Coalition_Age + Citizens_Advocacy_Group + Coalition_Dues + 
    Lobbying_Expenditures + Interest_Group_Crosses_Issue_Boundary + 
    Coalition_Faces_Legislative_Threat + Coalition_Focuses_on_Authorizing_Legislation + 
    Coalition_Steering_Committee + coal2, family = binomial, 
    data = dat)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0758  -0.7183  -0.4848   0.3900   2.5732  

Coefficients: (5 not defined because of singularities)
                                               Estimate Std. Error  z value
(Intercept)                                  -4.932e+13  7.410e+13   -0.666
absdiff                                      -1.286e-02  3.537e-02   -0.364
Partisan_Diversity                            3.963e+12  1.642e+13    0.241
Network_Embeddedness_Strong                   2.306e+01  5.386e+00    4.281
Interest_Group_Partisanship                   4.471e-02  2.042e-02    2.190
Coalition_Partisanship                        1.134e+13  8.274e+12    1.370
Interest_Group_Age                            2.934e-01  2.301e-01    1.275
Coalition_Age                                 2.099e+14  1.548e+14    1.356
Citizens_Advocacy_Group                       3.466e-01  2.113e-01    1.640
Coalition_Dues                               -5.419e+12  7.125e+12   -0.761
Lobbying_Expenditures                         9.489e-03  1.357e-02    0.699
Interest_Group_Crosses_Issue_Boundary        -1.266e-01  2.675e-01   -0.473
Coalition_Faces_Legislative_Threat           -2.500e+13  5.395e+13   -0.463
Coalition_Focuses_on_Authorizing_Legislation  9.680e+13  1.008e+14    0.960
Coalition_Steering_Committee                 -1.970e+13  2.051e+13   -0.960
coal22                                       -4.277e+13  5.389e+13   -0.794
coal23                                       -1.365e+14  1.127e+14   -1.210
coal24                                       -7.544e+13  9.447e+13   -0.799
coal25                                       -1.609e+14  1.252e+14   -1.285
coal26                                       -9.511e+13  8.399e+13   -1.132
coal27                                       -1.054e+14  7.717e+13   -1.365
coal28                                       -1.207e+14  8.916e+13   -1.354
coal29                                       -1.431e+14  1.199e+14   -1.193
coal210                                      -1.414e+14  1.098e+14   -1.288
coal211                                      -9.245e+13  9.845e+13   -0.939
coal212                                      -8.269e+13  7.841e+13   -1.055
coal213                                       8.804e+12  1.043e+13    0.844
coal214                                      -8.142e+13  1.010e+14   -0.806
coal215                                      -3.236e+13  2.604e+13   -1.243
coal216                                      -1.319e+14  9.713e+13   -1.358
coal217                                      -4.493e+13  3.964e+13   -1.133
coal218                                      -1.166e+14  9.725e+13   -1.199
coal219                                      -5.967e+13  5.863e+13   -1.018
coal220                                      -6.872e+13  1.014e+14   -0.678
coal221                                      -4.114e+13  9.756e+13   -0.422
coal222                                      -2.352e+12  2.246e+13   -0.105
coal223                                      -2.675e+13  2.649e+13   -1.010
coal224                                       4.220e+13  5.918e+13    0.713
coal225                                       3.789e+13  2.878e+13    1.316
coal226                                      -8.853e+13  6.512e+13   -1.360
coal227                                      -9.822e+13  8.975e+13   -1.094
coal228                                      -6.442e+13  5.459e+13   -1.180
coal229                                       7.921e+12  9.437e+13    0.084
coal230                                      -1.195e+14  1.023e+14   -1.168
coal231                                      -1.087e+13  2.729e+13   -0.398
coal232                                      -5.237e+13  3.863e+13   -1.356
coal233                                      -1.010e+14  9.899e+13   -1.020
coal234                                      -1.827e+13  1.366e+13   -1.338
coal235                                      -9.069e+12  6.650e+12   -1.364
coal236                                       2.232e+13  1.639e+13    1.362
coal237                                       1.443e+13  1.116e+13    1.293
coal238                                       4.823e+13  3.851e+13    1.252
coal239                                       1.728e+13  1.316e+13    1.314
coal240                                      -7.005e+13  8.451e+13   -0.829
coal241                                      -1.465e+14  1.069e+14   -1.370
coal242                                      -1.438e+14  1.161e+14   -1.239
coal243                                      -1.036e+14  9.976e+13   -1.039
coal244                                       1.037e+12  7.849e+11    1.322
coal245                                      -4.509e+15  1.676e+13 -269.045
coal246                                      -3.725e+13  3.348e+13   -1.113
coal247                                       6.723e+12  2.873e+13    0.234
coal248                                      -4.340e+13  3.927e+13   -1.105
coal249                                      -5.176e+13  5.286e+13   -0.979
coal250                                      -1.036e+14  8.356e+13   -1.240
coal251                                      -4.893e+13  9.130e+13   -0.536
coal252                                      -9.276e+12  1.173e+13   -0.791
coal253                                      -1.040e+13  8.567e+12   -1.214
coal254                                       1.233e+13  1.062e+13    1.161
coal255                                      -1.548e+13  2.384e+13   -0.649
coal256                                      -2.551e+14  1.964e+14   -1.299
coal257                                      -1.369e+14  1.306e+14   -1.048
coal258                                       3.911e+13  6.226e+13    0.628
coal259                                       1.859e+13  2.906e+13    0.640
coal260                                       1.959e+13  2.816e+13    0.695
coal261                                       5.165e+12  1.556e+13    0.332
coal262                                      -3.179e+13  3.611e+13   -0.880
coal263                                       2.972e+13  3.108e+13    0.956
coal264                                       4.962e+13  3.650e+13    1.360
coal265                                      -1.202e+14  1.136e+14   -1.058
coal266                                      -9.471e+13  1.131e+14   -0.837
coal267                                      -9.185e+13  1.018e+14   -0.903
coal268                                              NA         NA       NA
coal269                                              NA         NA       NA
coal270                                              NA         NA       NA
coal271                                      -1.392e+14  1.212e+14   -1.148
coal272                                              NA         NA       NA
coal273                                       4.015e+13  2.978e+13    1.348
coal274                                              NA         NA       NA
                                             Pr(>|z|)    
(Intercept)                                    0.5056    
absdiff                                        0.7161    
Partisan_Diversity                             0.8092    
Network_Embeddedness_Strong                  1.86e-05 ***
Interest_Group_Partisanship                    0.0285 *  
Coalition_Partisanship                         0.1707    
Interest_Group_Age                             0.2022    
Coalition_Age                                  0.1752    
Citizens_Advocacy_Group                        0.1009    
Coalition_Dues                                 0.4469    
Lobbying_Expenditures                          0.4844    
Interest_Group_Crosses_Issue_Boundary          0.6360    
Coalition_Faces_Legislative_Threat             0.6431    
Coalition_Focuses_on_Authorizing_Legislation   0.3369    
Coalition_Steering_Committee                   0.3369    
coal22                                         0.4274    
coal23                                         0.2261    
coal24                                         0.4245    
coal25                                         0.1987    
coal26                                         0.2575    
coal27                                         0.1721    
coal28                                         0.1758    
coal29                                         0.2328    
coal210                                        0.1976    
coal211                                        0.3477    
coal212                                        0.2916    
coal213                                        0.3987    
coal214                                        0.4203    
coal215                                        0.2140    
coal216                                        0.1744    
coal217                                        0.2571    
coal218                                        0.2305    
coal219                                        0.3088    
coal220                                        0.4978    
coal221                                        0.6732    
coal222                                        0.9166    
coal223                                        0.3125    
coal224                                        0.4758    
coal225                                        0.1880    
coal226                                        0.1739    
coal227                                        0.2738    
coal228                                        0.2380    
coal229                                        0.9331    
coal230                                        0.2428    
coal231                                        0.6904    
coal232                                        0.1752    
coal233                                        0.3078    
coal234                                        0.1810    
coal235                                        0.1726    
coal236                                        0.1731    
coal237                                        0.1961    
coal238                                        0.2105    
coal239                                        0.1889    
coal240                                        0.4072    
coal241                                        0.1706    
coal242                                        0.2152    
coal243                                        0.2989    
coal244                                        0.1863    
coal245                                       < 2e-16 ***
coal246                                        0.2658    
coal247                                        0.8150    
coal248                                        0.2691    
coal249                                        0.3275    
coal250                                        0.2150    
coal251                                        0.5920    
coal252                                        0.4289    
coal253                                        0.2247    
coal254                                        0.2455    
coal255                                        0.5163    
coal256                                        0.1939    
coal257                                        0.2945    
coal258                                        0.5299    
coal259                                        0.5222    
coal260                                        0.4867    
coal261                                        0.7400    
coal262                                        0.3787    
coal263                                        0.3390    
coal264                                        0.1740    
coal265                                        0.2899    
coal266                                        0.4025    
coal267                                        0.3667    
coal268                                            NA    
coal269                                            NA    
coal270                                            NA    
coal271                                        0.2509    
coal272                                            NA    
coal273                                        0.1777    
coal274                                            NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1211.03  on 1069  degrees of freedom
Residual deviance:  985.03  on  987  degrees of freedom
AIC: 1151

Number of Fisher Scoring iterations: 25

> 
> 
> # ==============================================================================
> # Micro-level interpretation (= predicted probabilities)
> # ==============================================================================
> 
> # create dyadic datasets
> edgeprob.3 <- edgeprob(model.3)
Warning messages:
1: In is.na(theta.offset[m$etamap$offsettheta]) :
  is.na() applied to non-(list or vector) of type 'NULL'
2: In edgeprob(model.3) :
  There are structural zeros or ones. For these dyads, the predicted probabilities are not valid and must be manually replaced by 0 or 1, respectively.
> edgeprob.4 <- edgeprob(model.4)
Warning messages:
1: In is.na(theta.offset[m$etamap$offsettheta]) :
  is.na() applied to non-(list or vector) of type 'NULL'
2: In edgeprob(model.4) :
  There are structural zeros or ones. For these dyads, the predicted probabilities are not valid and must be manually replaced by 0 or 1, respectively.
> edgeprob.10 <- edgeprob(model.10)
Warning messages:
1: In is.na(theta.offset[m$etamap$offsettheta]) :
  is.na() applied to non-(list or vector) of type 'NULL'
2: In edgeprob(model.10) :
  There are structural zeros or ones. For these dyads, the predicted probabilities are not valid and must be manually replaced by 0 or 1, respectively.
> edgeprob.16 <- edgeprob(model.16)
Warning messages:
1: In is.na(theta.offset[m$etamap$offsettheta]) :
  is.na() applied to non-(list or vector) of type 'NULL'
2: In edgeprob(model.16) :
  There are structural zeros or ones. For these dyads, the predicted probabilities are not valid and must be manually replaced by 0 or 1, respectively.
> edgeprob.17 <- edgeprob(model.17)
Warning messages:
1: In is.na(theta.offset[m$etamap$offsettheta]) :
  is.na() applied to non-(list or vector) of type 'NULL'
2: In edgeprob(model.17) :
  There are structural zeros or ones. For these dyads, the predicted probabilities are not valid and must be manually replaced by 0 or 1, respectively.
> 
> 
> # function for plotting facets with variable 1 conditional on variable 2
> facets <- function(edgeprobs, mem, number, var1, var2, varname1, varname2) {
+   # keep only those dyadic probabilities where there is no structural zero
+   include <- logical(nrow(edgeprobs))
+   for (r in 1:length(include)) {
+     if (mem[edgeprobs$i[r], edgeprobs$j[r] - nrow(mem)] == 1) {
+       include[r] <- TRUE
+     } else {
+       include[r] <- FALSE
+     }
+   }
+   dyads <- edgeprobs[include == TRUE, ]
+   
+   # cut network embeddedness into slices
+   dyads$v1 <- c(dyads[var1])[[1]]
+   v2 <- c(dyads[var2])[[1]]
+   v2.quantiles <- quantile(v2)
+   v2 <- cut(v2, v2.quantiles, labels = names(v2.quantiles)[2:5])
+   v2[is.na(v2)] <- "25%"
+   dta <- transform(dyads, v2 = v2)
+ 
+   # plot conditional probabilities with facets
+   pdf(paste0("facets.", number, ".", varname1, ".pdf"))
+   gp <- ggplot(data = dta, aes(x = v1, y = probability))
+   print(gp + stat_smooth(method = "lm", fullrange = TRUE, color = "black") + facet_wrap( ~ v2, 
+       ncol = 2) + xlab(varname1) + ylab("Probability") + ggtitle(
+       paste0("Model ", number, ": ", varname1, " effect conditional on ", 
+       varname2)))
+   dev.off()
+ }
> 
> facets(edgeprobs = edgeprob.3, mem = mem, number = 3, 
+        var1 = "edgecov.Partisan_Diversity[[i]]", 
+        var2 = "edgecov.Network_Embeddedness_Strong[[i]]", 
+        varname1 = "Partisan diversity", varname2 = "Network embeddedness")
null device 
          1 
> 
> facets(edgeprobs = edgeprob.3, mem = mem, number = 3, 
+        var2 = "edgecov.Partisan_Diversity[[i]]", 
+        var1 = "edgecov.Network_Embeddedness_Strong[[i]]", 
+        varname2 = "Partisan diversity", varname1 = "Network embeddedness")
null device 
          1 
> 
> facets(edgeprobs = edgeprob.4, mem = mem, number = 4, 
+        var1 = "edgecov.Partisan_Diversity[[i]]", 
+        var2 = "edgecov.Network_Embeddedness_Strong[[i]]", 
+        varname1 = "Partisan diversity", varname2 = "Network embeddedness")
null device 
          1 
> 
> facets(edgeprobs = edgeprob.4, mem = mem, number = 4, 
+        var2 = "edgecov.Partisan_Diversity[[i]]", 
+        var1 = "edgecov.Network_Embeddedness_Strong[[i]]", 
+        varname2 = "Partisan diversity", varname1 = "Network embeddedness")
null device 
          1 
> 
> facets(edgeprobs = edgeprob.10, mem = mem, number = 10, 
+        var1 = "edgecov.Partisan_Diversity[[i]]", 
+        var2 = "edgecov.commdensity[[i]]", varname1 = "Partisan diversity", 
+        varname2 = "Communication density")
null device 
          1 
> 
> facets(edgeprobs = edgeprob.10, mem = mem, number = 10, 
+        var2 = "edgecov.Partisan_Diversity[[i]]", 
+        var1 = "edgecov.commdensity[[i]]", varname2 = "Partisan diversity", 
+        varname1 = "Communication density")
null device 
          1 
> 
> 
> # predicted probabilities for visibility interaction (model 16)
> include.16 <- logical(nrow(edgeprob.16))
> for (r in 1:length(include.16)) {
+   if (mem[edgeprob.16$i[r], edgeprob.16$j[r] - nrow(mem)] == 1) {
+     include.16[r] <- TRUE
+   } else {
+     include.16[r] <- FALSE
+   }
+ }
> dyads.16 <- edgeprob.16[include.16 == TRUE, ]
> 
> dta <- data.frame(prob = dyads.16$probability,
+                   pd = dyads.16$`edgecov.Partisan_Diversity[[i]]`,
+                   vis = dyads.16$`edgecov.Visibility[[i]]`)
> 
> pdf(paste0("interaction.visibility.16.pdf"))
> gp <- ggplot(data = dta, aes(x = pd, y = prob, linetype = factor(vis))) +
+     stat_smooth(method = "lm", fullrange = TRUE, colour = "black")
> gp + labs(linetype = "Visibility") + xlab("Partisan diversity") +
+     ylab("Probability") + ggtitle(paste("Partisan diversity conditional on", 
+     "visibility of the coalition"))
> dev.off()
null device 
          1 
> 
> 
> # predicted probabilities for controversy interaction (model 17)
> include.17 <- logical(nrow(edgeprob.17))
> for (r in 1:length(include.17)) {
+   if (mem[edgeprob.17$i[r], edgeprob.17$j[r] - nrow(mem)] == 1) {
+     include.17[r] <- TRUE
+   } else {
+     include.17[r] <- FALSE
+   }
+ }
> dyads.17 <- edgeprob.17[include.17 == TRUE, ]
> 
> dta <- data.frame(prob = dyads.17$probability,
+                   pd = dyads.17$`edgecov.Partisan_Diversity[[i]]`,
+                   cv = dyads.17$`edgecov.Controversial[[i]]`)
> 
> pdf(paste0("interaction.controversy.17.pdf"))
> gp <- ggplot(data = dta, aes(x = pd, y = prob, linetype = factor(cv))) +
+     stat_smooth(method = "lm", fullrange = TRUE, colour = "black")
> gp + labs(linetype = "Controversy") + xlab("Partisan diversity") +
+     ylab("Probability") + ggtitle(paste("Partisan diversity conditional on",
+                                         "controversialness of the coalition"))
> dev.off()
null device 
          1 
> 
> 
> # ==============================================================================
> # Marginal effects plot using btergm and interplot
> # ==============================================================================
> 
> pdf("marginal-effects-model3a.pdf", width = 6, height = 4)
> marginalplot(model.3, 
+              var1 = "edgecov.Partisan_Diversity", 
+              var2 = "edgecov.Network_Embeddedness_Strong", 
+              inter = "edgecov.Diversity_X_Embeddedness_Strong", 
+              structzeromat = as.matrix(nonmem), 
+              ylab = "Partisan diversity", 
+              xlab = "Network embeddedness", 
+              rug = TRUE) + ggtitle("Model 3") + theme_bw()
> dev.off()
null device 
          1 
> 
> pdf("marginal-effects-model3b.pdf", width = 6, height = 4)
> marginalplot(model.3, 
+              var1 = "edgecov.Network_Embeddedness_Strong", 
+              var2 = "edgecov.Partisan_Diversity", 
+              inter = "edgecov.Diversity_X_Embeddedness_Strong", 
+              structzeromat = as.matrix(nonmem), 
+              xlab = "Partisan diversity", 
+              ylab = "Network embeddedness", 
+              rug = TRUE) + ggtitle("Model 3") + theme_bw()
> dev.off()
null device 
          1 
> 
> pdf("marginal-effects-model4a.pdf", width = 6, height = 4)
> marginalplot(model.4, 
+              var1 = "edgecov.Partisan_Diversity", 
+              var2 = "edgecov.Network_Embeddedness_Strong", 
+              inter = "edgecov.Diversity_X_Embeddedness_Strong", 
+              structzeromat = as.matrix(nonmem), 
+              ylab = "Partisan diversity", 
+              xlab = "Network embeddedness", 
+              rug = TRUE) + ggtitle("Model 4") + theme_bw()
> dev.off()
null device 
          1 
> 
> pdf("marginal-effects-model4b.pdf", width = 6, height = 4)
> marginalplot(model.4, 
+              var1 = "edgecov.Network_Embeddedness_Strong", 
+              var2 = "edgecov.Partisan_Diversity", 
+              inter = "edgecov.Diversity_X_Embeddedness_Strong", 
+              structzeromat = as.matrix(nonmem), 
+              xlab = "Partisan diversity", 
+              ylab = "Network embeddedness", 
+              rug = TRUE) + ggtitle("Model 4") + theme_bw()
> dev.off()
null device 
          1 
> 
> pdf("marginal-effects-model10a.pdf", width = 6, height = 4)
> marginalplot(model.10, 
+              var1 = "edgecov.Partisan_Diversity", 
+              var2 = "edgecov.commdensity", 
+              inter = "edgecov.commdensity.diversity", 
+              structzeromat = as.matrix(nonmem), 
+              ylab = "Partisan diversity", 
+              xlab = "Communication density", 
+              rug = TRUE) + ggtitle("Model 10") + theme_bw()
> dev.off()
null device 
          1 
> 
> pdf("marginal-effects-model10b.pdf", width = 6, height = 4)
> marginalplot(model.10, 
+              var1 = "edgecov.commdensity", 
+              var2 = "edgecov.Partisan_Diversity", 
+              inter = "edgecov.commdensity.diversity", 
+              structzeromat = as.matrix(nonmem), 
+              xlab = "Partisan diversity", 
+              ylab = "Communication density", 
+              rug = TRUE) + ggtitle("Model 10") + theme_bw()
> dev.off()
null device 
          1 
> 
> pdf("marginal-effects-model16.pdf", width = 6, height = 4)
> marginalplot(model.16, 
+              var1 = "edgecov.Partisan_Diversity", 
+              var2 = "edgecov.Visibility", 
+              inter = "edgecov.Diversity_X_Visibility", 
+              structzeromat = as.matrix(nonmem), 
+              ylab = "Partisan diversity", 
+              xlab = "Visibility of the coalition", 
+              point = TRUE, 
+              rug = FALSE) + 
+   ggtitle("Model 16") + 
+   theme_bw()
> dev.off()
null device 
          1 
> 
> pdf("marginal-effects-model17.pdf", width = 6, height = 4)
> marginalplot(model.17, 
+              var1 = "edgecov.Partisan_Diversity", 
+              var2 = "edgecov.Controversial", 
+              inter = "edgecov.Diversity_X_Controversial", 
+              structzeromat = as.matrix(nonmem), 
+              ylab = "Partisan diversity", 
+              xlab = "Controversy", 
+              point = TRUE, 
+              rug = FALSE) + 
+   ggtitle("Model 17") + 
+   theme_bw()
> dev.off()
null device 
          1 
> 
> 
> # save workspace to a file for later use
> save.image(file = "leadership-lobbying.RData")
> 
> proc.time()
    user   system  elapsed 
3416.396  108.760 2017.602 
